Analysis of cognitive impairment using MRI

Abstract

A method and apparatus are provided for use as part of a diagnostic analysis of a patient's psychiatric or neurological health based on the use of a normative database of fMRI data results and cognitive task results for a population of previous patients. An embodiment proposes to acquire a specific set of fMRI data of one or more regions of the patient's brain while the patient is performing a cognitive task, and at the same time record the results of the patient's performance in the cognitive task. A combination of both the cognitive task results and the fMRI data for the brain regions is used to provide an indication regarding the patient's cognitive health. The normative database of fMRI data results and cognitive task results for a population of patients is used as a reference. It may store results for both healthy and pathological patients to provide normal and abnormal references.

Description

[Technical field]

[0001] The present invention relates to an apparatus for aiding in the diagnosis of neurological disorders using MRI data. [Background technology]

[0002] Magnetic resonance imaging (MRI) scanners rely on a large static magnetic field (Bo) to align the nuclear spins of atoms as part of the procedure to generate images in a patient's body. These images can reflect various quantities or properties of the subject. For example, the hemodynamic response of brain activation causes magnetic and electrical changes in activated brain regions.

[0003] In brain imaging, MRI can be used to image the structure of the brain or to image the functional activity of the brain. The latter is known as functional MRI (fMRI). Functional MRI measures brain activity by magnetically detecting localized regional fluctuations associated with blood flow. Cerebral blood flow and neuronal activation are coupled, and thus changing blood flow in a localized region indicates brain activity in that region. In other words, when an area of ​​the brain is in use, blood flow to that area also increases.

[0004] There are different specific metrics of neural activation that can be derived from fMRI data. The primary form of fMRI uses blood oxygen level dependent (BOLD) contrast imaging. Other approaches include arterial blood spin labeling and diffusion MRI. The latter procedure is similar to BOLD fMRI, but provides contrast based on the magnitude of diffusion of water molecules in the brain.

[0005] With regard to BOLD contrast imaging, it is based on detecting the difference in magnetic properties between arterial (oxygen-rich) and venous (oxygen-poor) blood. When neurons in a given brain region become active, the local blood flow in that region increases, and after a certain delay of about 2 seconds, oxygen-rich (oxygenated) blood starts to replace oxygen-depleted (deoxygenated) blood. Blood flow continues to increase for 4-6 seconds, rising to a peak, and then decreasing to a lower level at or just below the original level. Deoxygenated hemoglobin (dHb) is more magnetic (paramagnetic) than oxygenated hemoglobin (Hb), the latter being effectively resistant to magnetism (diamagnetic). This difference leads to an improvement in the MR signal from oxygenated brain regions, since diamagnetic blood in oxygenated regions interferes less with the magnetic MR signal. This improvement can be mapped as a function of location in the brain and as a function of time to map which neurons are active at any given time.

[0006] Functional MRI can be used as an investigative tool in the field of neurological and psychiatric disorders and associated brain injuries. It involves administering to a patient one or more cognitive tasks or tests that are known to activate specific areas of the brain in specific patterns in healthy subjects.

[0007] In the field of neurological and psychiatric diseases, it is known to use cognitive tests or tasks as diagnostic tools in the differential diagnosis of psychiatric disorders.

[0008] However, performance impairments in a given task may be associated with multiple different psychiatric disorders or may be associated with physical brain damage. With regard to the latter, performance impairments in a given cognitive test or task may be caused by underlying damage in a variety of different locations in the brain and / or by various types of acquired or neurodegenerative brain damage, such as traumatic brain injury (TBI), cerebrovascular accident (CVA), mild cognitive impairment (MCI) or Alzheimer's disease (AD). A psychiatric disorder may also be associated with performance impairments in a particular cognitive task, even in the absence of physical brain damage.

[0009] Moreover, it is known that brain lesions in different brain locations can show similar results in cognitive tests. Similarly, it is known that brain lesions in the same location can cause different results in cognitive tests. In this regard, see, for example, the article "Network localization of heterogeneous neuroimaging findings" by Darby, RR, Joutsa, J., & Fox, MD (2019) (Brain, 142(1), 70-79).

[0010] In addition, cognitive tests typically involve various cognitive functions, i.e., performing a cognitive test requires the recruitment of different cognitive functions. Indeed, if a particular cognitive function is impaired as a result of brain damage, the individual can still perform cognitive tests in a standard manner, since they can compensate for the deficit. As a result, the results of cognitive tests can be the same for two people with very different underlying deficits and different causes of these deficits, simply because they utilized different underlying cognitive functions and networks to solve the task. This is particularly relevant for cases of mild brain diseases, such as mild TBI (which accounts for 85% of all TBIs) and mild cognitive impairment (MCI), which is considered a precursor to dementia. As a result, the results of cognitive tests (batteries) do not specifically reflect the underlying brain damage that causes the cognitive impairment. This can lead to misdiagnosis and underdiagnosis of cognitive disorders.

[0011] The use of MRI images as an additional diagnostic tool in differentially diagnosing psychiatric disorders and / or brain injuries is known. Summary of the Invention [Problem to be solved by the invention]

[0012] Typically, structural MRI imaging is used to detect or rule out brain injury. However, it may not be able to detect injuries with subtle structural features, nor can it detect neurological deficits caused by injuries that have no associated structural presentation.

[0013] As an additional diagnostic tool in investigating brain injury and psychiatric disorders, it is known to use functional MRI imaging to evaluate neural activity in patients at a regional level. However, results can be difficult to interpret, as whole patterns of brain regions may typically be activated during the performance of a task, making it difficult to discriminate from this alone what may cause the presentation of symptoms. [Means for solving the problem]

[0014] The invention is defined by the claims.

[0015] According to an example according to an aspect of the present invention, A processing device comprising: acquiring functional MRI (fMRI) data about a patient corresponding to one or more regions of the brain, the fMRI data being acquired while the patient is performing a predetermined cognitive task; acquiring cognitive task outcome data for the patient corresponding to the cognitive task administered during acquisition of the fMRI data; deriving neural activity data from the acquired fMRI data; retrieving from the data store a pre-acquired database that stores baseline neurological activity data and baseline cognitive task result data for a population of people (e.g., former patients) that are associated with the neurological or psychiatric disorder of interest and that are associated with the predetermined cognitive task; comparing the patient's activity data and task result data to the baseline activity data and baseline task result data; providing an output to a user interface based on the comparison; and A processing device is provided having one or more processors adapted to execute the

[0016] Embodiments of the present invention propose to use a combination of patient cognitive task results and local fMRI-derived neural activity data to provide an output that aids in the diagnosis of neurological or psychiatric disorders.

[0017] This approach involves a novel data analysis step not currently performed in clinical practice, which involves comparing fMRI-derived neurological activity data and / or brain imaging data of patients performing cognitive tasks with an electronic normative database of previous patients performing the same tasks.

[0018] Furthermore, the use of a combination of fMRI data and cognitive task outcome data to provide a more specific analysis of a patient's condition is currently unknown. This addresses a clinical need by improving the accuracy of diagnosis of neurological or psychiatric disorders, as well as a wide variety of types of acquired and neurodegenerative brain injuries and associated cognitive disorders. The result is the promise of improved diagnosis. This can be used in practice to facilitate more personalized treatment, as well as personalized prediction of the time progression of cognitive disorders (and associated risk factors).

[0019] In some embodiments, fMRI data may be obtained for one or more predetermined brain regions, the brain regions being associated with a predetermined neurological or psychiatric disorder of interest, and the derived neurological activity data may be activity data associated with each of the one or more brain regions.

[0020] The predetermined brain regions referred to above may correspond to predetermined neural networks in the brain associated with the neurological or psychiatric disorder of interest.

[0021] Cognitive task outcome data is acquired simultaneously with the acquisition of the fMRI data based on patient responses while administering the cognitive task.

[0022] In some embodiments, the processing device is further adapted to derive a diagnostic indicator of the neurological or psychiatric disorder based on the comparison and provide the diagnostic indicator as output to the user interface. This may be based on applying an algorithm to derive the indicator. It may be based on applying a machine learning algorithm.

[0023] In some embodiments, the output to the user interface indicates the results of the comparison.

[0024] In some embodiments, the neurological activity data is blood oxygen level dependent (BOLD) contrast data.

[0025] An objective according to at least one set of embodiments is to determine the type, location and severity of any cognitive impairment and / or associated brain damage, if any. This can be achieved by providing a number of different databases in a database store for different neurological or psychiatric disorders and potentially different cognitive tasks, and comparing the acquired data to normative data in each of these databases.

[0026] In some embodiments, the data store may store multiple different databases, each storing baseline activity data and baseline cognitive task result data associated with a different particular combination of a neurological or psychiatric disorder of interest and an administered cognitive task.

[0027] The processing device can query the data store according to the neurological or psychiatric disorder of interest and the cognitive task to which the newly acquired outcome data corresponds.

[0028] The process is selecting (e.g., operator selected) a neurological or psychiatric disorder of interest from a predefined set of different disorders; For the selected disorder, further selecting one of a set of one or more cognitive tests / tasks related to the selected disorder; identifying (e.g., using a look-up table or other criteria) a set of one or more brain regions of interest associated with the disorder; acquiring fMRI data associated with those brain regions while administering selected tests / tasks to the patient; acquiring a result of the cognitive task based on the subject's indicators / responses during the task simultaneously with the acquisition of the fMRI data; obtaining a database from the data store, the database storing baseline neurological activity data and cognitive task result data for the selected disorder and the selected cognitive task; Includes.

[0029] In one particular set of embodiments, the processing unit may be adapted to apply multiple different cognitive tasks to probe the same single cognitive impairment. For example, the processing unit may be adapted to:

[0030] acquiring a plurality of fMRI data sets relating to the patient, each acquired whilst a different cognitive task is being administered to the patient; obtaining a cognitive task result for said patient for each administered cognitive task; retrieving databases from said data store, each database relating to a different cognitive task and further relating to a neurological or psychiatric disorder of interest; for each cognitive task, comparing activity data derived from the corresponding fMRI data set with baseline activity data in the corresponding retrieved database and comparing the cognitive task results with baseline cognitive task results in the corresponding database; The method may be adapted to perform a combination of:

[0031] In other words, multiple cognitive tasks are administered and the results compared to normative data to more reliably determine whether a patient suffers from a neurological or psychiatric disorder of interest.

[0032] The method may further include providing a differential diagnostic indicator based on the above process.

[0033] In some embodiments, the database may include at least a first database portion including data related to a first population of people (e.g., former patients) not suffering from the neurological or psychiatric disorder of interest, and a second database portion including data related to a second population of people suffering from the neurological or psychiatric disorder. The processing device may be adapted to compare the derived neurological activity data and the obtained task result data with data in both the first and second database portions.

[0034] The reference neurological activity data referred to in this disclosure may include brain activity maps for one or more regions of the brain for a population of people to which the reference data corresponds. The reference neurological activity data may additionally or alternatively include statistical summary data derived from the neurological activity data of the population of people. In addition to the reference neurological activity data, the database may further include brain imaging data of the population and / or may include statistical summary data derived from brain imaging data of the population, e.g., one or more average brain images of the population.

[0035] The baseline neurological activity data and baseline task outcome data may include statistical data pre-calculated from activity data and task outcome data for a population of prior patients. The statistical data may comprise one or more statistical metrics, such as the mean, median, interquartile range, or any other statistical metric.

[0036] In some embodiments, the processing unit may be adapted to update the database based on the acquired neurological activity data for the patient and the task result data for the patient.

[0037] This may involve simply adding the obtained neurological activity data and cognitive task result data to the database, or may further involve recalculating statistical data stored by the database, such as updating averages or other statistical metrics.

[0038] In some embodiments, the data in the database may be stratified based on one or more demographic categories of the population of people to which the data relates, for example, based on gender, age, and / or other predetermined characteristics.

[0039] The processing unit may be further adapted to obtain an indication of one or more demographic categories for the patient under examination, and to filter the searched database according to the patient demographic categories before performing the comparison.

[0040] In some embodiments, a processing unit may be adapted to control the execution of a series of cognitive tasks and acquire fMRI associated with each task, following a decision tree structure, where the results of each cognitive task determine how, if any, further tasks should be administered, for example to facilitate narrowing down a patient diagnosis.

[0041] Thus, in some embodiments, the processing unit may be further adapted to apply a selection algorithm for determining a new cognitive test to be administered to the patient depending on the acquired cognitive task results and / or depending on the acquired neurological activity data.

[0042] The processing device may be further adapted to retrieve from the data store a normative database including baseline activity data and baseline cognitive task result data associated with the selected cognitive task.

[0043] The processing unit may be further adapted to control the patient interface control module to manage the selected cognitive task and to generate control signals for controlling the MRI imaging device to acquire second fMRI data of the patient while the selected cognitive task is being administered.

[0044] According to some embodiments, acquiring the fMRI data may include a processing device generating control signals for controlling the MRI imaging device to acquire the fMRI data, while in other embodiments the processing device may simply passively receive the fMRI data without controlling its acquisition.

[0045] A further aspect of the present invention provides a system comprising a processing device according to any embodiment described in the present disclosure and an MRI imaging device operatively coupled to the processing device for acquiring fMRI data of a patient.

[0046] Another aspect of the present invention is a method for producing a acquiring functional MRI (fMRI) data on a patient corresponding to one or more regions of the brain, the fMRI data being acquired while the patient is performing a predetermined cognitive task; acquiring cognitive task outcome data for the patient corresponding to the cognitive task administered while acquiring the fMRI data; deriving neurological activity data from the acquired fMRI data; retrieving from the data store a pre-acquired normative database storing baseline neurological activity data and baseline cognitive task result data associated with the neurological or psychiatric disorder of interest and the predetermined cognitive task for a population of prior patients; comparing the patient's neurological activity data and task outcome data to baseline neurological activity data and baseline task outcome data; providing an output to a user interface based on the comparison; and The present invention provides a computer-implemented method comprising:

[0047] Another aspect of the present invention provides a computer program product including computer program code configured, when executed by a processor operatively coupled to an MRI imaging device and a user interface, to cause the processor to perform the method outlined above, or according to any embodiment described in this disclosure.

[0048] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0049] For a better understanding of the invention and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings in which: [Brief description of the drawings]

[0050] [Figure 1] 1 illustrates a processing device and system according to one or more embodiments of the present invention. [Diagram 2] 1 illustrates an exemplary system according to one or more embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0051] The present invention will now be described with reference to the drawings.

[0052] It should 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 should be understood that the figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the figures to denote the same or similar parts.

[0053] The present invention provides a method and apparatus for use as part of a diagnostic analysis of a patient's psychiatric or neurological health. An embodiment proposes acquiring fMRI data of a specific set of one or more regions of a patient's brain while the patient is performing a cognitive task, and simultaneously recording the results of the patient's performance in the cognitive task. A combination of both the cognitive task results and the fMRI data for the brain regions is used to provide an indication regarding the patient's cognitive health. More specifically, a normative database of fMRI data results and cognitive task results for a population of patients is used as a reference. It may store results for both healthy and non-healthy patients to provide normal and abnormal references.

[0054] By way of background, it has been found that poor cognitive task performance may be associated with neurological or psychiatric disorders, see for example the article "Behavioral and Neural Correlates of Disturbed Orienting Attention in Post-Traumatic Stress Disorder" by Russman Block, S., King, AP, Sripada, RK, Weissman, DH, Welsh, R., & Liberzon, I. (2017) (Cognitive, Affective, & Behavioral Neuroscience, 17(2), 422-436).

[0055] In this paper, we describe a study using fMRI images and attention network tasks (to examine attention and executive control). It found that fMRI data from patients suffering from PTSD (Post-Traumatic Stress Disorder) showed greater resting-state functional connectivity in the attention network during performance of an attention network task. This information can be used to inform the patient's differential diagnosis regarding PTSD. In particular, if the patient's attention is impaired, this is presented both as a below-average performance in the attention network task as well as a deviation from normal brain activation patterns during performance of this task. If patients' performance on a task is reduced but they show normal brain activation during performance of the task, they are likely to have another underlying deficit causing the reduced performance. In that case, the reduced performance on the attention network task may be due to an impairment of a cognitive function or network other than attention that is also required to perform the task, e.g., executive function. This is presented briefly as an example to illustrate the concept.

[0056] More generally, embodiments of the present invention propose methods and systems for improved detection of cognitive impairments and / or associated brain damage (if any) based on functional neuroimaging and cognitive testing. The approach is based on imaging specific brain regions and networks that are active during the performance of specific cognitive tests or tasks. Methods and systems are proposed that can be used for clinical decision support in neuroimaging diagnosis and treatment. Detection of specific brain damage and associated cognitive impairments is optimized by comparing both cognitive task performance and brain activity in relevant brain regions with a normative fMRI database. Methods and systems integrating data from functional neuroimaging data and cognitive testing are not yet used in clinical practice and would solve a clinical need by helping to improve the accuracy of diagnosis and prediction of progression over time of a wide range of acquired and neurodegenerative brain injuries and associated cognitive disorders.

[0057] It should also be noted that current approaches to detecting subtle cognitive impairment and associated brain damage are general, not patient-specific. In other words, current approaches do not take into account individual identification information. Therefore, a further objective according to at least one set of embodiments is to provide a method that allows for more personalized diagnostic indications and also allows for prediction of the progression of cognitive impairment (and associated risk factors) over time.

[0058] FIG. 1 illustrates generally an exemplary system 10 in accordance with at least one set of embodiments of the present invention.

[0059] The system 10 comprises a processing device 20. The processing device includes an input / output 22 and a set of one or more processors 24.

[0060] The one or more processors 24 are adapted, according to at least one operating mode, to implement a method comprising the following steps:

[0061] The method includes acquiring functional MRI (fMRI) data 28 of a patient corresponding to one or more predefined brain regions of the patient. The one or more brain regions may be selected to be associated with a particular neurological or psychiatric disorder of interest, or a standard set of one or more brain regions may be selected. The fMRI data is data acquired while the patient is performing a predefined cognitive task. The fMRI data may be acquired from an MRI imaging device 30 operably coupled to a processing device input / output 22 as shown in FIG. 1. In other words, the data is received by the processing device in real time or near real time as it is acquired. The processing device is shown operably coupled to the input / output 22 for this purpose. Alternatively, the data may be received from a data store, the data having been acquired at an earlier time.

[0062] With regard to acquiring data for one or more specific brain locations, this may be set by the operator of the MRI imaging device or may be set automatically by the control module of the MRI imaging device 30 .

[0063] Control of the MRI imaging device is most typically performed by a separate local control module residing in the MRI imaging device, however, it is also an option for control of the MRI imaging device to be performed by the processing device 20. For example, the processing device may be an integrated control module that performs operational control of the MRI device 30 and also performs the data processing steps that are part of the main inventive concepts outlined in this disclosure.

[0064] With regard to the performance of cognitive tasks or tests, this may be performed using a patient indicator subsystem 32, which typically includes a sensory output device with a display mounted within the bore tunnel of the MRI machine, and headphones, usually for the subject to wear. The presentation of task or test prompts to the patient may be controlled by a separate control module instead of being controlled by the processing unit 20 (as shown diagrammatically in FIG. 1 ), or in some cases may be controlled by a control module of the MRI imaging machine. The cognitive tasks or tests are administered to the patient simultaneously with the acquisition of the fMRI data.

[0065] The processing device is further adapted to acquire cognitive task result data of the patient corresponding to the cognitive task administered during acquisition of the fMRI data. Administration of the cognitive task includes presenting a sequence of one or more sensory prompts to the user. It typically further includes detecting one or more responses from the user. These responses may be indicated directly by the user, for example, via a user control device, such as a handheld control device having one or more buttons. In other cases, responses may be issued verbally by the user and entered manually by the MRI operator using a computer console or other input device. References to task results may include raw response data from the user and / or may include performance data, meaning an indication of success in a test or task, for example, a number of correct answers, or any other performance metric derived from the responses.

[0066] The processing device is further adapted to derive neurological activity data associated with each of the one or more brain regions from the acquired fMRI data. This may comprise, for example, BOLD data for each of the one or more regions. The activity data may include local spatial activity mapping for each area, or may simply include a single activity metric value for each area. The activity data may include an indication of brain activity in each region as a function of time.

[0067] The processing device is further adapted to retrieve from the data store 34 a pre-acquired normative database 36 that stores baseline neurological activity data and baseline cognitive task result data related to the neurological or psychiatric disorder of interest and related to a given cognitive task for a population of patients. This may include pre-calculated statistical summary data from the activity data and task result data for the population of patients. The statistical data may comprise one or more statistical metrics, such as the mean, median, interquartile range, or any other statistical metric.

[0068] The processing device is further adapted to compare the patient's activity data and task outcome data with baseline activity data and baseline task outcome data.

[0069] The processing device is further adapted to provide an output 23 to a user interface 26 based on the comparison. There are various options for the output provided, as outlined in more detail below. The output may simply include a display of the results of the comparison. This alone provides a useful diagnostic aid, especially if the database includes a database portion relating to healthy patients and a second database portion relating to abnormal patients, and the processing device compares the acquired data with both database portions. In other examples, a diagnostic indicator may be generated by further processing based on the comparison, for example based on an algorithm such as a decision tree algorithm, or based on one or more thresholds related to the comparison results, or based on a machine learning algorithm.

[0070] For example, the processing device may calculate the degree of deviation of the patient's neurological activity from the average neurological activity level recorded in the database, and / or the degree of deviation of the patient's cognitive task results from one or more average values ​​recorded in the database. One or more predefined thresholds may be applied to one or both of the derived degrees of deviation to thereby determine whether the patient has a neurological or psychiatric disorder of interest. As described in more detail below, in some embodiments, multiple normative databases may be used, each associated with a different neurological or psychiatric disorder of interest. By determining the degree of deviation of the patient's activity and cognitive task result data using the normative data in each database, a determination may be made as to which, if any, of the relevant neurological or psychiatric disorders the patient is suffering from.

[0071] One aspect of the invention provides a processing device only.

[0072] Another aspect of the invention provides a computer-implemented method including the above steps described as being performed by a processor.

[0073] A further aspect of the invention provides a system comprising a processing device 20 and an MRI imaging device 30 operatively coupled to the processing device for acquiring fMRI data of a patient. The system may further comprise a user interface 26. The system may further comprise any other of the components shown in FIG. 1 or described elsewhere in this disclosure.

[0074] The system may further include a database storing a number of different cognitive tasks or tests to administer to the patient, from which the system can select. This may be stored in the same data store 34 used to store the database 36 of normative data, or in a separate data store.

[0075] To further illustrate the acquisition of fMRI data, Figure 2 illustrates an exemplary system 10 according to one or more embodiments, in which the MRI imager 30 is shown in greater detail. Figure 2 illustrates the MRI imager 30 operatively coupled with a processing unit 20. Any other components of the system 10 are not shown for ease of illustration.

[0076] The MRI imaging device 30 comprises a magnet 304. The magnet 304 is a superconducting cylindrical magnet with a type bore 306 through it. Different types of magnets can be used, for example both split cylindrical magnets and so-called open magnets. Split cylindrical magnets are similar to standard cylindrical magnets, except that the cryostat is split into two sections to allow access to the magnet's isoplane, and such magnets can be used, for example, in conjunction with charged particle beam therapy. Open magnets have two magnet sections, one above the other, with a space between them large enough to accommodate the subject, i.e. an arrangement of the two section areas similar to the area of ​​a Helmholtz coil. Open magnets are popular because they are less confining to the subject. Inside the cryostat of the cylindrical magnet is a collection of superconducting coils. Within the bore 306 of the cylindrical magnet 304 is an imaging zone 308 where the magnetic field is strong and sufficiently uniform to perform magnetic resonance computed tomography. A region of interest 309 is shown within the imaging zone 308. Typically, magnetic resonance data is acquired about a region of interest. A subject 318 is shown supported by a subject support 320 such that at least a portion of the subject 318 is within the imaging zone 308 and the region of interest 309.

[0077] Also within the magnet bore 306 are a set of magnetic field gradient coils 310 used for preliminary magnetic resonance data acquisition to spatially encode magnetic spins within the imaging zone 308 of the magnet 304. The gradient coils 310 are connected to a gradient coil power supply 312. The magnetic field gradient coils 310 are shown only diagrammatically. Typically, the magnetic field gradient coils 310 include three separate coil sets for spatial encoding in three orthogonal spatial directions. The gradient power supply supplies electrical current to the gradient coils. The electrical current supplied to the magnetic field gradient coils 310 is controlled as a function of time and may be ramped or pulsed.

[0078] Adjacent to the imaging zone 308 is a radio frequency coil 314 for steering the orientation of magnetic spins in the imaging zone 308 and for receiving radio transmissions from the spins in the imaging zone 308. In this example, the radio frequency coil 314 is a head coil and the region of interest 309 images the brain of a subject 318.

[0079] The radio frequency antenna may include multiple coil elements. The radio frequency antenna may also be referred to as a channel or antenna. The radio frequency coil 314 is connected to a radio frequency transceiver 316. The radio frequency coil 314 and the radio frequency transceiver 316 may be replaced by separate transmitting and receiving coils, as well as separate transmitters and receivers. It should be understood that the radio frequency coil 314 and the radio frequency transceiver 316 are shown only diagrammatically. The radio frequency coil 314 is also intended to represent a dedicated transmitting antenna and a dedicated receiving antenna. Similarly, the transceiver 316 may represent a separate transmitter and receiver. The radio frequency coil 314 may also have multiple receiving / transmitting elements, and the radio frequency transceiver 316 may have multiple receiving / transmitting channels. For example, when a parallel imaging technique such as sensing is performed, the radio frequency coil 314 will have multiple coil elements.

[0080] Within the bore 306 of the magnet 304 is a patient indicator 32. The subject indicator may, for example, provide audio and / or visual cues to the subject 318. The patient indicator 32 may provide cues or prompts to a user. The patient indicator 32 may, for example, have a light visible to the subject 318, may be a display, or may provide an audio signal. The transceiver 316, gradient controller 312, and patient indicator 32 are shown as connected to the hardware interface 106 of the computer system 102.

[0081] The patient indicators 32 are used in administering cognitive tasks to the subject.

[0082] The patient indicator 32 may further include an input device, such as a handheld button or control, that allows the patient to indicate a response as part of the cognitive task.

[0083] The medical imaging device is further shown as comprising a computer 102 comprising a processor 104. The processor is shown as being connected to an optional hardware interface 106 and an optional user interface 108. The user interface 108 may be or include a display for rendering images. The hardware interface 106 may be, for example, a network interface or may be used to exchange data or commands with other components of the medical imaging system.

[0084] The processor 104 is further shown as being connected to a memory 110. The memory 110 may be any combination of memory accessible to the processor 104. This may include main memory, cache memory, and non-volatile memory such as flash RAM, a hard drive, or other storage device, etc. In some examples, the memory 104 may be considered to be a non-transitory computer-readable medium.

[0085] The memory 110 is shown as including machine-executable instructions 120. The machine-executable instructions 120 enable the processor 104 to perform various data processing tasks, and in some examples, to control other components of the medical imaging system 100. The machine-executable instructions 120 enable the processor 104 to perform at least a portion of the present methods.

[0086] The memory 110 is further shown as including pulse sequence commands 330. The pulse sequence commands are either commands or data that can be converted into such commands that enable the processor 104 to control the magnetic resonance imaging system 302. The memory 110 is further shown as including magnetic resonance imaging data 332 acquired by controlling the magnetic resonance imaging system 302 with the pulse sequence commands 330.

[0087] Next, implementation of the system according to a set of preferred embodiments will be described.

[0088] According to a first set of embodiments, representing a simple case, a single cognitive task is administered to the patient while in the MRI device 30, and fMRI data is acquired of the patient during performance of the task. Neurological activity data is derived from the fMRI data for each of one or more predetermined brain regions scanned. An appropriate database of baseline activity and cognitive task result data is retrieved from the data store 34, which corresponds to the particular combination of the administered cognitive task and the neurological or psychiatric disorder under investigation. As mentioned above, the baseline data in the data store is used as a comparator and an output is generated which may be an indication of the result of the comparison, or may be a diagnostic indication obtained based on the comparison, for example based on the use of a diagnostic algorithm or model.

[0089] Preferably, in addition to the fMRI data, structural MRI data is also acquired for a set of one or more brain regions of interest when scanning a patient with MRI imager 30. Preferably, normative database 36 further stores normative structural data for a population of patients that can be used as a comparator for structural MRI data obtained for a new patient.

[0090] Acquiring the fMRI data may include the processing unit 20 generating control signals for controlling the MRI imager 30 to acquire the fMRI data, or the acquisition of the data may be controlled by a separate control module of the MRI imager.

[0091] The particular brain region or regions to be scanned may be selected by an operator of the MRI imager 30, or may be automatically selected by the system, for example, based on a look-up table or algorithm, which may be based on input from the operator indicating a particular neurological or psychiatric disorder of interest and / or a cognitive task to be administered. Each disorder will typically be associated with particular brain regions that may be affected by the disorder, and each cognitive task may be associated with particular brain regions that function in performing the task, and more specifically, particular neural networks within those regions that function in performing the task.

[0092] Regarding the cognitive tasks or tests administered, a wide variety of cognitive tasks or tests are known in the art that are relevant for testing the functioning of specific neural networks and types of cognitive activity. In this case, it may be particularly interesting to administer one or more tests that measure attention and executive functions, since these cognitive functions are often impaired in patients with subtle cognitive impairment, for example due to mild traumatic brain injury, aging, or small blood vessel diameters in the brain.

[0093] More generally, the cognitive tests administered may include those to assess cognitive function across a variety of cognitive domains, including, for example, one or more of attention, executive function, memory, perception, motor function, and language.

[0094] As mentioned above, the system 10 may include a data store 34 that stores a database 36 that holds a number of different cognitive tasks or tests for administration to a patient, from which one or more selections may be made.

[0095] Each stored cognitive task or test may effectively include a computer routine or program represented by a set of computer program code that, when executed by at least one processor of the system, causes a predetermined sequence of prompts to be communicated to the patient via the patient indicator subsystem 32. These may include any combination of visual prompts, audio prompts, tactile prompts, or any other sensory prompts. The prompts may ask the patient to perform a particular cognitive function in their mind. Additionally or alternatively, it may ask the patient to provide one or more responses, which may be given, for example, verbally or through control of a user input device.

[0096] The responses form the basis of the patient's outcome for the cognitive task. Either the raw responses are used as the outcome of the task, or the responses can be compared to a baseline or reference (e.g., a set of correct answers) and the outcome is provided by comparing the patient's response to this baseline or reference. The outcome is recorded either manually by an operator or automatically.

[0097] The cognitive tasks or tests are preferably preselected to have high sensitivity. The cognitive tests can be selected to be compatible and validated for use during neuroimaging. Fine-tuning of the tests can be performed over time based on specific clinical needs and insights gained from research. Thus, the tests are editable.

[0098] In some examples, the cognitive task or test administered to the patient may be selected based in part on one or more demographic categories of the patient, such as age and education level. Alternatively, rather than varying the cognitive task administered based on demographic categories, one or more parameters of a given selected task may be adjusted according to one or more characteristics of the patient, such as age and education level. This provides for the formation of an individualized test that can reflect differences in cognitive abilities and / or reflect cognitive deficits that the patient may have. Thus, in other words, the level of difficulty of a given test may be adjusted based on the characteristics of the patient.

[0099] In a preferred example, the normative database 36 comprises at least a first database portion containing normative data relating to a first population of patients not suffering from the neurological or psychiatric disorder of interest, and a second database portion containing normative data relating to a second population of patients suffering from the neurological or psychiatric disorder. Although the database portions are referred to, the normative data of the healthy and pathological populations may in fact be stored in separate databases. In other words, for each combination of cognitive task and neurological or psychiatric disorder of interest, there may be a database or database portion containing neuroimaging and cognitive test performance data from healthy individuals, as well as a database or database portion containing neuroimaging and cognitive test performance data from individuals with brain damage. Both databases may be stratified, for example, based on age, sex, education level, and / or other demographic factors, and based on diagnosis.

[0100] The processing unit is adapted to compare the derived activity metric data and the resulting task result data with data in both the first and second database portions.

[0101] Regarding the data stored in the normative database 36, there are different options. Multiple functional (and optionally structural) neuroimaging data records for an entire population of patients may be stored. Alternatively, each database may store one or more statistical metrics for an entire population of patients, e.g., the mean, or the interquartile range, etc. The type of data stored in the normative database may be image data. Alternatively, it may be, for example, numerical data indicating activity levels in one or more brain regions, optionally forming one or more statistical metrics associated with the data, e.g., the mean activity level for one or more brain regions, for a population of patients. The neurological activity data may be, for example, blood oxygen level dependent (BOLD) contrast data. In the latter case, the comparison mentioned above may include comparing the derived activity levels for each scanned brain region with the mean level or range or threshold value stored in the database for each brain region. For example, a patient's fMRI data may be considered to match normative data in the database where the patient's BOLD activity is within one standard deviation of the norm (i.e., the mean) in the database.

[0102] The patient population to which the data in each database corresponds may include patients from a variety of different demographic groups (age, gender, and education level) and may also include information regarding the patient's clinical condition.

[0103] Normative database 36 further includes data indicative of the results of the cognitive tasks to which it corresponds for the same reference population of patients. Again, this may include individual data records for each patient in the population, or may include statistical metrics about the task results for the entire population, or for demographically stratified subsegments of the population.

[0104] The second set of preferred embodiments is the same as the first embodiment, except that, as briefly discussed as an option above, data store 34 stores a number of different normative databases, each corresponding to a different combination of the neurological or psychiatric disorder of interest and the cognitive task or test administered when acquiring the data.

[0105] This set of embodiments allows at least two additional advantageous functionalities. First, the operator or system effectively has a choice as to the particular cognitive task administered to test for a particular neurological or psychiatric disorder, and also has a choice from many different neurological or psychiatric disorders to test for. Thus, the systems and methods in this set of embodiments allow a wider range of options for testing subjects and analyzing their cognitive health. A second additional functionality following the first functionality is that the system or operator can choose to administer multiple different cognitive tests (and acquire corresponding fMRI data sets for each) as part of the testing of a single given neurological or psychiatric disorder, and / or can select the testing of multiple different neurological or psychiatric disorders. In the latter case, the system can administer a series of tests (known as a test battery) including tests related to different neurological or psychiatric disorders, as a means of aiding in differential diagnosis between different neurological or psychiatric disorders.

[0106] Thus, in this set of embodiments, the data store stores a number of different normative databases, each storing baseline activity data and baseline cognitive task result data associated with a different particular combination of the neurological disorder of interest and the cognitive task being administered.

[0107] The processing unit 20 can query the database according to the particular neurological disorder and cognitive task to which the newly acquired outcome data corresponds.

[0108] According to this second set of embodiments in which, in use, only a single cognitive task is administered, or at least only one at a time, the process performed may be summarized as follows.

[0109] The process is selecting (e.g., operator selected or computer program selected) a disorder of interest from a predefined set of different disorders; For the selected disorder, further selecting one of a set of one or more cognitive tests / tasks related to the selected disorder; identifying a set of one or more brain regions of interest associated with the disorder; acquiring fMRI data associated with those brain regions while administering selected tests / tasks to the patient; acquiring a result of the cognitive task based on an indication of a subject during the task, concurrently with acquiring the fMRI data; retrieving from the data store a database storing statistical results about neural activity and cognitive task results for a selected disorder and a selected cognitive task; deriving from the acquired fMRI data neurological activity data associated with each of the one or more brain regions; comparing the patient's activity data and task outcome data with baseline activity data and baseline task outcome data; may include.

[0110] The above process may be repeated multiple times for each of multiple different neurological or psychiatric disorders of interest, such that differential diagnostic indications can be derived between different neurological or psychiatric disorders of interest.

[0111] If a number of different tests (i.e. a series of tests) are to be performed, in summary the following process may be as follows:

[0112] obtaining a plurality of fMRI data sets for the patient, each acquired while a different cognitive task is administered to the patient; and obtaining cognitive task results for the patient for each administered cognitive task; searching the data store for normative databases each associated with a different cognitive task and further associated with a neurological or psychiatric disorder; For each cognitive task, compare activity data derived from the corresponding fMRI data set with baseline activity data in the corresponding retrieved database, and compare cognitive task results with baseline cognitive task results in the corresponding database. It can be adapted as follows.

[0113] The multiple cognitive tests may all relate to the same neurological or psychiatric disorder of interest or to different disorders of interest.

[0114] The method may further include providing a differential diagnostic indicator based on the above process.

[0115] In some embodiments, when multiple cognitive tests are administered to a patient, each new test administered may be selected by the processing device 20 based on performance on previously administered cognitive tests and associated neuroimaging results. This may be done post-assessment (or immediately, if possible, in real-time) based on cognitive test performance and brain activation patterns. For example, if a patient exhibits normal performance on tests assessing attention, but abnormal brain activation patterns, this may indicate the use of different cognitive functions to perform the cognitive tests suggesting compensation of impaired cognitive functions with non-impaired cognitive functions. In this case, it may be recommended to administer additional cognitive tests assessing the cognitive functions used to perform the attention tests, as evident from the brain activation patterns.

[0116] According to at least some examples, the processing device 20 is adapted to apply a selection algorithm to determine a new cognitive test to be administered to the patient depending on the acquired cognitive task results and / or depending on the acquired neural activity data. The processing device may be further adapted to retrieve a normative database from the data store including baseline activity data and baseline cognitive task result data associated with the selected cognitive task. The processing device may be further adapted to generate control signals for controlling a patient interface control module (e.g., a control module of the patient indicator subsystem 32 described above) to manage the selected cognitive task, and for controlling an MRI imaging device to acquire second fMRI data of the patient while the selected cognitive task is being administered.

[0117] The selection algorithm may, for example, be a predetermined decision tree algorithm, where the results of one cognitive task and / or associated neurological activity data are used by the algorithm to determine the next task to be administered in a sequential hierarchical decision structure.

[0118] According to any of the above-described embodiments, the data in normative database 36 is preferably stratified based on one or more demographic categories of the patient population to which the data relates. These demographic categories may include, for example, age, sex, occupation, education level, and / or other demographic factors. The data may be further stratified based on additional characteristics, such as medical history, or factors related to lifestyle habits (e.g., alcohol consumption, smoking).

[0119] In this case, the processing unit may be further adapted to obtain an indication of one or more demographic categories for the patient, and to filter the normative database according to the patient's demographic categories before performing the comparison.

[0120] Thus, comparisons are made between similar groups of patients accounting for gender, age, and other predetermined characteristics.

[0121] As described above, the neural activity data and cognitive task outcome data can be compared to both a portion of the database corresponding to healthy individuals and a portion of the database corresponding to individuals with a particular disorder. Multimodal information can be stored for each individual in a normative database. Comparisons can be made using stratified samples as described above.

[0122] According to any of the set of preferred embodiments described above, the method includes generating an output for a user interface based on a comparison of the activity data and the cognitive task result data to the baseline data in each of the one or more databases 36. There are various options for the output that may be generated and methods for deriving it.

[0123] In some examples, the output to the user interface 26 simply includes a representation of the results of the comparison. For example, results are presented on the user interface 26 showing cognitive test performance compared to normative samples represented in the normative database 36, optionally corrected for relevant characteristics such as age and education level, and also showing neural activity in different brain regions compared to the normative samples represented in the database.

[0124] In a further example, additional processing may be performed to determine a diagnostic indicator of one or more neurological or psychiatric disorders based on the comparison and provide the diagnostic indicator as output to a user interface.

[0125] In some examples, the results of the comparison may be processed using a diagnostic algorithm or model to determine one or more of the patient's neurological or psychiatric disorder, the type and location of cognitive impairment in the brain, and the type and location of any brain injury.

[0126] In some examples, the results of the comparison can be processed algorithmically to generate an assessment of the patient's cognitive function and brain damage, including which cognitive domains and brain regions and networks are impaired, as well as an assessment of the severity of the impairment. The assessment can also include an indication of one or more compensatory mechanisms that the patient can use to avoid any identified deficits.

[0127] The user interface 26, to which the generated output is communicated, as described above, may comprise one or more sensory output components. These may include one or more of a display device, an audio output device, and a haptic output device. The user interface may further include one or more user input components, such as a keypad and / or a pointer device. The user interface may comprise a touch screen display in some embodiments.

[0128] The output provided to the user interface 26 may include an indication of the results of the aforementioned comparisons performed between the patient's data and the reference data in the database 36 .

[0129] In a preferred embodiment, fMRI image data acquired for the patient is presented in real time on the display of user interface 26. Preferably, a visual representation of the derived neurological activity data is also presented on the display, e.g., overlaid on the fMRI image data or provided as part of the fMRI image data.

[0130] In a preferred embodiment, a visual representation of a comparison between the patient's neurological activity in one or more scanned brain regions and the activity represented in the normative database 36 may be provided on the display. For example, two images may be presented side-by-side on the screen, one representing a visual map or representation of the neurological activity in the patient's brain as a function of location and / or time, and one representing a visual map or representation of the average neurological activity as a function of location and / or time for a population of patients represented in the database 36. The average image may be an average of a sub-strata of the population, filtered according to the demographic classification of the patients, as described above. The display may further show images of the average neurological activity for both healthy populations and populations diagnosed with a neurological or psychiatric disorder of interest. Thus, the operator may easily compare the patient's results with those of healthy and non-healthy populations.

[0131] In addition to presenting the comparison of neurological activity, the user interface 26 also presents the calculated comparison results, for example showing the deviation of the patient's activity level from the average of a healthy population and / or from the average of a non-healthy population. This may be provided in the form of a numerical indication of the disparity, for example a number of standard deviations away from the average in each case.

[0132] In addition to presenting a comparison of the neurological activity results, the user interface 26 also preferably provides an indication of a comparison of the patient's cognitive task results with the average of the results for the same task for a population of patients in the database 36. Again, this comparison is preferably performed and results presented for populations of both healthy and non-healthy individuals, and specifically for a substratum of the database 36 population that matches the patient's demographic classification.

[0133] As mentioned above, in the case where multiple cognitive tests (for the same and / or different neurological or psychiatric disorders) are administered and data obtained for each patient, the above comparison results may be presented for each patient in turn. A GUI may be provided on the display of the user interface 26, allowing the operator to switch between different sets of results and review each at will. The operator may also select other relevant comparisons, for example comparisons with patient groups having different diagnoses from the patient.

[0134] According to any of the above-described embodiments, in a preferred example, each newly acquired data set of fMRI data and cognitive task results can be used to update the normative database used as a comparator. For example, this can include simply adding the activity data derived from the fMRI scan data to the normative database 36 along with the cognitive task result data. In other cases, it can include updating statistical metrics stored in the database based on the new data, for example updating the average activity levels in each brain region based on the newly acquired data.

[0135] Thus, in this set of embodiments, the normative data is time-updated, which means that the normative database grows over time as more individuals are scanned, ensuring greater representativeness of the normative database.

[0136] According to any of the above-mentioned embodiments, it is advantageous to include a function for predicting the progression of the patient's condition. By way of example, according to one or more embodiments, a multimodal (or multifactorial) assessment can be performed based on the application of a progression algorithm to predict the time progression of cognitive impairment. The prediction can be based on cognitive tests, neuroimaging findings, and other disease-specific data.

[0137] For example, in the area of ​​traumatic brain injury (TBI), predictions may be based on one or more of pre-injury physical and mental comorbidities, early post-concussion symptoms, early post-traumatic stress, headache in the ED, nausea / vomiting in the ED, neck pain in the ED, emotional distress at 2 weeks, maladaptive coping at 2 weeks, intoxication at time of injury, type of injury, age, education level, cause of injury, and score on the Glasgow Coma Scale.

[0138] In the area of ​​neurodegenerative diseases, clustering assessment can be based on one or more of cognitive testing, neuroimaging findings, patient characteristics such as age, sex and education level, lifestyle factors such as levels of physical activity and smoking, genetic testing, and blood tests, cerebrospinal fluid, etc.

[0139] The progression prediction itself may be performed using a statistical prediction model, such as, for example, a pre-trained machine learning algorithm. The machine learning algorithm may be trained using a training database that includes data records relating to a plurality of patients.

[0140] According to any of the above-mentioned embodiments, the above-mentioned data analysis steps can be preferably performed in real-time. In particular, deriving neural activity data, comparing the activity data and the task result data with the reference data, and deriving the output based on the comparison can be performed in real-time with the acquisition of the fMRI data. This allows the results to be presented to the operator of the system on the user interface 26 simultaneously with the scanning of the patient. An advantage of this real-time analysis is that the parameters of the test can be adjusted based on the results. For example, the real-time analysis can be used by the operator to fine-tune the cognitive test, e.g., to adjust the difficulty level and / or to adjust technical parameters, to prevent the use of direct compensation mechanisms by the patient.

[0141] To further illustrate the concepts of the present invention, three examples of neurological or psychiatric disorders to which embodiments of the present invention may be advantageously applied are outlined below. These examples are purely illustrative and non-limiting.

[0142] The first application is for the assessment of depressive stress. The paper "Decreased default mode network functional connectivity in patients with recurrent major depression" by Yan et al. (2019) (PNAS, 116, 9078-9083) is referred to. A study using fMRI data from 848 patients with major depressive disorder (MDD) and 794 normal control patients is outlined. In the MDD cohort, the fMRI data showed reduced functional connectivity in the default mode network, confirming the important role of the default mode network in MDD. Reduced functional connectivity in the default mode network was positively associated with symptom severity in recurrent MDD. Thus, if the psychiatric disorder of interest is depression, the cognitive task administered may be one that tests the default model functional network.

[0143] A second application example is for the assessment of claustrophobia. See the paper "Spontaneous brain activity after recall of fear conditioning using resting-state functional MRI" by Feng, Zheng & Feng (2015) (Scientific reports, 5, 16701). They outlined a study that investigated spontaneous neuronal activity after memory activation of a fear (e.g., claustrophobia). It reported significantly enhanced functional connectivity between the amygdala and the ventromedial prefrontal cortex upon fear memory activation. Thus, if the psychiatric disorder of interest is claustrophobia, the cognitive task administered to the patient may prompt the user to recall a fear memory, and the brain regions scanned include the area between the amygdala and the prefrontal cortex.

[0144] A third application example is the assessment of task-related stress, which may be associated with a range of neurological or psychiatric disorders, including, for example, mild traumatic brain injury. During sustained periods of demanding cognitive workload accompanied by cognitive fatigue, humans typically display time-on-task effects, with performance steadily worsening over the period of task engagement. See Lim et al. (2010) in their paper "Imaging brain fatigue from sustained mental workload" (An ASL perfusion study of time-on-task effects, Neuroimage, 49, 3426-3435). In this paper, we report sustained effects of cognitive fatigue in the frontoparietal network after periods of intense mental work, and demonstrate the key role of this attention network in mediating time-on-task effects.

[0145] The above-described embodiments of the invention use a processing device. The processing device may generally comprise a single processor or multiple processors. It may be located in a single containing device, structure, or unit, or may be distributed among multiple different devices, structures, or units. Thus, a reference to a processing device being adapted or configured to perform a particular step or task may correspond to that step or task being performed by any one or more of multiple processing components, alone or in combination. Those skilled in the art will understand how such a distributed processing device can be implemented. The processing device includes a communication module or input / output for receiving data and outputting data to further components.

[0146] The processor or processors of a processing device may be implemented in a number of ways using software and / or hardware to perform the various functions required. A processor typically uses one or more microprocessors that can be programmed using software (e.g., microcode) to perform the necessary functions. A processor may 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.

[0147] 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).

[0148] In various implementations, a 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 executed on the one or more processors and / or controllers, perform the necessary functions. The various storage media may be fixed within a processor or controller, or may be portable, such that one or more programs stored thereon may be loaded into the processor.

[0149] 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 article "a" or "an" does not exclude a plurality.

[0150] A single processor or other unit may fulfill the functions of several items recited in the claims.

[0151] 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.

[0152] The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, provided together with or as part of other hardware, but may also be distributed in other forms, such as over the Internet or other wired or wireless telecommunications systems.

[0153] It should be noted that when the term "adapted for" is used in the claims or description, it is intended to be equivalent to the term "configured for."

[0154] Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. A processing device comprising: acquiring functional MRI (fMRI) data about a patient corresponding to one or more regions of the brain, the fMRI data being acquired while the patient is performing a predetermined cognitive task; acquiring cognitive task outcome data for the patient corresponding to the cognitive task administered during acquisition of the fMRI data; deriving neural activity data from the acquired fMRI data; retrieving from the data store a pre-acquired database that stores baseline neurological activity data and baseline cognitive task result data related to the neurological or psychiatric disorder of interest and related to said predetermined cognitive task for a population of people; comparing the patient's activity data and task result data to the baseline activity data and baseline task result data; providing an output to a user interface based on the comparison; and 23. A processing device having one or more processors adapted to execute the steps of:

2. The processing device of claim 1 , wherein the processing device is further adapted to derive a diagnostic indicator of the neurological or psychiatric disorder of interest based on the comparison and provide the diagnostic indicator as the output to the user interface.

3. 3. A processing device according to claim 1 or 2, wherein the output to the user interface indicates a result of the comparison.

4. 4. The processing device of claim 1, wherein the neurological activity data is blood oxygen level dependent (BOLD) contrast data.

5. 5. The processing device of claim 1, wherein the data store stores a plurality of different databases, each storing baseline activity data and baseline cognitive task result data associated with a different particular combination of neurological disorder of interest and administered cognitive task.

6. The processing device includes: acquiring a plurality of fMRI data sets relating to the patient, each acquired whilst a different cognitive task is being administered to the patient; obtaining a cognitive task result for said patient for each administered cognitive task; retrieving databases from said data store, each database relating to a different cognitive task and further relating to a neurological or psychiatric disorder of interest; for each cognitive task, comparing activity data derived from the corresponding fMRI data set with baseline activity data in the corresponding retrieved database and comparing the cognitive task results with baseline cognitive task results in the corresponding database; The processing device of claim 5 , adapted to execute the following:

7. 7. The processing device of claim 1 , wherein the database comprises at least a first database portion containing data relating to a first population of people not suffering from the neurological or psychiatric disorder of interest and a second database portion containing data relating to a second population of people suffering from the neurological or psychiatric disorder.

8. 8. The processing device of claim 1, wherein the baseline activity data and baseline task result data comprise pre-calculated statistical data from activity data and task result data for a population of people.

9. 9. The processing device of claim 1, wherein the processing device is adapted to update the database based on acquired activity data for the patient and task result data for the patient.

10. 10. A processing device according to any preceding claim, wherein the data in the database is stratified based on one or more demographic categories of the population of people to which the data relates.

11. The processing device is further adapted to obtain an indication of one or more demographic categories for the patient; the processing unit is adapted to filter the database according to a demographic classification of the patient before performing the comparison; The processing device according to claim 10.

12. The processing device further comprises: applying a selection algorithm to determine a new cognitive test to be administered to the patient depending on the acquired cognitive task results and / or depending on the acquired neurological activity data; retrieving from the data store a database including baseline activity data and baseline cognitive task result data associated with the selected cognitive task; controlling a patient interface control module to manage the selected cognitive task and generating a control signal for controlling an MRI imaging device to acquire second fMRI data of the patient while the selected cognitive task is being administered; 12. The processing device of claim 1, further adapted to execute the following:

13. 13. The processing device of claim 1, wherein acquiring the fMRI data comprises generating, by the processing device, a control signal for controlling an MRI imaging device to acquire the fMRI data.

14. A processing device according to any one of claims 1 to 13, an MRI imaging device operatively coupled to said processing device for acquiring fMRI data of said patient; The system has:

15. A processor operatively coupled to the MRI imaging device and the user interface, when executed, causes the processor to: acquiring functional MRI (fMRI) data on a patient corresponding to one or more regions of the brain, the fMRI data being acquired while the patient is performing a predetermined cognitive task; acquiring cognitive task outcome data for the patient corresponding to the cognitive task administered while acquiring the fMRI data; deriving neurological activity data from the acquired fMRI data; retrieving from the data store a pre-acquired database storing baseline neurological activity data and baseline cognitive task result data associated with a neurological or psychiatric disorder of interest and a predetermined cognitive task for a population of people; comparing the patient's activity data and cognitive task result data to the baseline activity data and baseline cognitive task result data; providing an output to a user interface based on the comparison; and 2. A computer program product comprising computer program code configured to cause the computer to perform a method comprising:

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