A device for generating scores indicating cognitive disorders such as Attention Deficit Hyperactivity Disorder (ADHD).

Abstract

The present disclosure relates to generating a score indicative of a subject having a cognitive disorder, such as a cognitive impairment, such as attention deficit hyperactivity disorder (ADHD), where the score can be generated based on electroencephalography or magnetoencephalography (EEG / MEG) data by calculating one or more correlation values ​​between pairs of time series determined based on EEG / MEG data. The present disclosure includes receiving a subject's EEG / MEG data and determining a first plurality of time series based on the EEG / MEG data, each of the first plurality of time series corresponding to a respective source location located within the subject's cranial cavity. Next, a first correlation value is calculated for the first pair of time series, and the first pair of time series is included in the determined first plurality of time series. A score is generated based on the first correlation values, and the score is output to a physician who can diagnose the subject and indicate that the subject has a cognitive disorder, such as a cognitive impairment, such as ADHD.

Description

[Background technology]

[0001] Bayesian brain theory establishes that the entire cortex represents a probability distribution, which collapses into an estimate only when a decision is needed. Nevertheless, how this transition from a probabilistic brain to a certainty or one-time decision occurs is not well understood. Therefore, harmonizing the stimulus-limited surprise (Shannon surprise) and deriving a metric that allows for the calculation of the information gain (Bayesian surprise) between prior and subsequent beliefs remains unresolved. Expressing the probability of an event in terms of its information (e.g., the -log function of the aforementioned probability) (also known as the Shannon surprise) can mean a transition from a probabilistic environment to a deterministic environment, where we are actually surprised by the occurrence of the event, and where the event has already occurred; therefore, the generated information bits can yield precision, i.e., cancel out uncertainty about the occurrence of the event. Thus, it may be necessary to distinguish between the precision bits of the Shannon information and the Shannon entropy bits representing uncertainty.

[0002] In attention research, it is assumed that a saliency map generated by the sensory saliency of an object is modulated by a top-down behavioral recognition signal to generate a priority map. While much research has been done on neural coding (e.g., through computational modeling of attention), adequate processing of top-down attentional signals has not been achieved. Instead, much of the conventional modeling research focuses on the bottom-up component of visual attention.

[0003] Furthermore, due to the complexity and heterogeneity of cognitive disorders such as attention deficit hyperactivity disorder (ADHD), the gap between neurobiology and the symptoms of such disorders has often been large, and consequently, the underlying neurocognitive mechanisms are often unknown.

[0004] Therefore, it may be advantageous to provide an improved system for generating a score indicating that a subject has a cognitive disorder such as attention deficit hyperactivity disorder (ADHD), a corresponding computer-readable storage medium, and a corresponding computer-administered method. [Overview of the project]

[0005] This disclosure relates to the generation of a score indicating a subject having a cognitive disorder, such as a cognitive impairment like attention deficit hyperactivity disorder (ADHD). In particular, the score can be generated based on EEG / MEG data by calculating one or more correlation values ​​between pairs of time series determined based on electroencephalogram (EEG) or magnetoencephalogram (EEG / MEG) data. The apparatus, system, computer-readable storage medium, and computer implementation method described herein generate a score indicating that a subject has a cognitive disorder, such as a cognitive impairment like attention deficit hyperactivity disorder (ADHD).

[0006] According to this disclosure, the device is configured to receive first EEG / MEG data from a subject. The device is further configured to determine a first set of time series based on the EEG / MEG data, each of which corresponds to a respective source location within the subject's cranial cavity. Furthermore, the device is configured to calculate a first correlation value for pairs of first time series, each of which is included in the determined first set of time series. The device is further configured to generate a score based on the first correlation value, the score indicating that the subject has a cognitive disorder, such as a cognitive impairment like attention deficit hyperactivity disorder (ADHD). Furthermore, the device is configured to output the score.

[0007] According to one embodiment, the source location of each of the first time series in the first pair of time series may be located in the frontal lobe, such as the middle frontal gyrus (MFG) and / or inferior frontal gyrus (IFG), in the cranial cavity of the subject, and the source location of each of the second time series in the first pair of time series may be located in the superior parietal lobe (SPL) of the cranial cavity of the subject.

[0008] In a further embodiment, the device may be further configured to calculate a second correlation value for a second pair of time series, the second pair of time series being included in the first set of determined time series. A score can be generated based on the first and second correlation values. In this embodiment, the source location of each of the first time series in the second pair of time series may be located in a ventral attention network area, such as the temporoparietal junction (TPJ) of the subject's cranial cavity, and the source location of each of the second time series in the second pair of time series may be located in a dorsal attention network area, such as the superior parietal lobe (SPL) of the subject's cranial cavity.

[0009] In one embodiment, the device may be further configured to calculate a third correlation value for a third pair of time series, the third pair of time series being included in the first set of time series being determined. A score can be generated based on the first, second, and third correlation values. Furthermore, the source location of each of the first time series in the third pair of time series may be located in a ventral attention network area, such as the temporoparietal junction (TPJ) of the subject's cranial cavity, and the source location of each of the second time series in the second pair of time series may be located in a ventral visual pathway area, such as the inferior temporal gyrus (ITG) of the subject's cranial cavity.

[0010] In a further embodiment, the device may be further configured to receive a second EEG / MEG data of a subject and determine a second set of time series based on the second EEG / MEG data, each of which corresponds to a respective source location within the subject's cranial cavity. Furthermore, the device may be further configured to calculate a fourth correlation value for a fourth set of time series pairs, each of which is included in the determined second set of time series, and each of which corresponds to the same respective source locations as the first set of time series pairs. Furthermore, the device may be configured to calculate a comparison value between the fourth correlation value and the first correlation value. A score can then be generated based on the comparison value.

[0011] In an embodiment, the step of determining the first plurality of time series may include filtering the first EEG / MEG data such that only the first plurality of time series contain a subset of all available time series, the subset including a pair of first time series, a pair of second time series, and a pair of third time series.

[0012] In an embodiment, the system may comprise an apparatus according to any of the embodiments described above, further comprising an EEG or MEG device for measuring at least first EEG or MEG data of a subject, and a task presentation device for presenting one or more tasks to the subject while the EEG / MEG device is measuring at least first EEG / MEG data of the subject.

[0013] According to this disclosure, a computer-administered method includes the step of receiving first electroencephalogram or magnetoencephalogram (EEG / MEG) data of a subject. The method further includes the step of determining a first set of time series based on the first EEG / MEG data, wherein each of the first set of time series corresponds to a respective source location located within the subject's cranial cavity. Furthermore, the method includes the step of calculating a first correlation value for a pair of the first set of time series, wherein the pair of the first set of time series is included in the determined first set of time series. The method further includes the step of generating a score based on the first correlation value, wherein the score indicates that the subject has a cognitive disorder such as ADHD, and the step of outputting the generated score.

[0014] According to the embodiment, the source location of each of the first time series in the first pair of time series may be located in the frontal lobe, such as the middle frontal gyrus (MFG) and / or inferior frontal gyrus (IFG) of the subject's cranial cavity, and the source location of each of the second time series in the first pair of time series may be located in the superior parietal lobe (SPL) (340) of the subject's cranial cavity.

[0015] According to the embodiment, the method may further include the step of calculating a second correlation value for a second pair of time series, wherein the second pair of time series is included in a plurality of first time series determined. A score can be generated based on the first and second correlation values. In this embodiment, the source location of each of the first time series in the second pair of time series may be located in a ventral attention network area such as the temporoparietal junction (TPJ) of the subject's cranial cavity, and the source location of each of the second time series in the second pair of time series may be located in a dorsal attention network area such as the superior parietal lobe (SPL) of the subject's cranial cavity.

[0016] In a further embodiment, the method may further include the step of calculating a third correlation value for a third pair of time series, wherein the third pair of time series is included in a determined first set of time series. A score can be generated based on the first, second, and third correlation values. Furthermore, the source location of each of the first time series in the third pair of time series may be located in a ventral attention network area such as the temporoparietal junction (TPJ) of the subject's cranial cavity, and the source location of each of the second time series in the second pair of time series may be located in a ventral visual pathway area such as the inferior temporal gyrus (ITG) of the subject's cranial cavity.

[0017] According to the embodiment, the method may further include the steps of receiving second EEG / MEG data of a subject, and determining a second set of time series based on the second EEG / MEG data, wherein each of the second set of time series corresponds to a respective source location located within the subject's cranial cavity. Furthermore, the method may further include the step of calculating a fourth correlation value for a fourth set of time series pairs, wherein the fourth set of time series pairs are included in the determined second set of time series, and the fourth set of time series pairs correspond to the same respective source locations as the first set of time series pairs. Furthermore, the method may include the step of calculating a comparison value between the fourth correlation value and the first correlation value. A score can be generated based on the comparison value.

[0018] According to one embodiment, the step of determining a first set of time series may include filtering the first EEG / MEG data such that only the first set of time series include a subset of all available time series in the first EEG / MEG data, the subset including a pair of first time series, a pair of second time series, and a pair of third time series.

[0019] The embodiments described above can be combined with each other. The embodiments can also be implemented in a computer-readable storage medium that includes computer-readable instructions causing the processor to perform the steps when executed by the processor.

[0020] This summary is provided to briefly introduce the selection of concepts that will be further described below in the detailed explanation. This summary is not intended to identify any important or essential features of the claimed subject matter, nor is it intended to be used to help determine the scope of the claimed subject matter.

[0021] These and further aspects and features of the present disclosure will be described below in detail with reference to the attached drawings.

Brief Description of the Drawings

[0022] [Figure 1] It is a diagram showing the Shannon brain model according to an embodiment of the present disclosure. [Figure 2] It is a diagram showing the dorsal-ventral attention network according to an embodiment of the present disclosure. [Figure 3] It is a diagram showing conditional probability and information by an exemplary task (“Task 2”) performed by a subject while EEG or MEG data is being measured from the subject. [Figure 4] It is a diagram showing experimental results showing links having significantly different PLV values in a comparison between two conditions of the same task (“Task 1”) measured from healthy subjects. [Figure 5] It is a diagram showing experimental results showing links having significantly different PLV values in a comparison between two conditions of two respective tasks (“Task 1” and “Task 2”) measured from healthy subjects. [Figure 6] It is a diagram showing experimental results showing links having significantly different PLV values in a comparison between two conditions of two respective tasks (“Task 1” and “Task 2”) measured from subjects with ADHD. [Figure 7] It is a diagram showing experimental results showing links of the central executive network having significantly different PLV values in a comparison between healthy subjects and subjects with ADHD for the conditions of a task (“Task 1”). [Figure 8] It is a diagram showing experimental results showing links of the salience network having significantly different PLV values in a comparison between healthy subjects and subjects with ADHD for the conditions of a task (“Task 1”). [Figure 9] It is a diagram showing experimental results showing links of the central executive network having significantly different PLV values in a comparison between healthy subjects and subjects with ADHD for the conditions of a task (“Task 2”). [Figure 10]This figure shows experimental results illustrating links in the central executive network that have significantly different PLV values ​​when comparing healthy subjects and subjects with ADHD under the conditions of Task 2. [Figure 11] This figure shows experimental results illustrating a link in the spleness network, where healthy subjects and subjects with ADHD have significantly different PLV values ​​for the conditions of the task ("Task 1"). [Figure 12] This figure shows experimental results illustrating links in the central executive network that have significantly different PLV values ​​when comparing healthy subjects and subjects with ADHD under the conditions of Task 1. [Figure 13] This figure shows an exemplary system according to one or more embodiments of the present disclosure. [Figure 14] This figure shows an exemplary data analysis device according to an embodiment of the present disclosure. [Figure 15] This is a diagram showing the brain inside the skull of a subject. [Figure 16] This figure shows the method according to the present disclosure embodiment. [Figure 17] The block diagram shows various exemplary components of an attention detection system for accurately detecting attention deficits, according to some embodiments of the present disclosure. [Figure 18A] This is a table of exemplary tasks for attention deficit detection according to some embodiments of the present disclosure. [Figure 18B] This is a table of exemplary tasks for attention deficit detection according to some embodiments of the present disclosure. [Figure 19] This figure shows the connectivity network between brain regions according to some embodiments of the present disclosure. [Figure 20-1] This flowchart shows the operation of an exemplary method for detecting attention deficit according to some embodiments of the present disclosure. [Figure 20-2] This flowchart shows the operation of an exemplary method for detecting attention deficit according to some embodiments of the present disclosure. [Figure 20-3]This flowchart shows the operation of an exemplary method for detecting attention deficit according to some embodiments of the present disclosure. [Figure 20-4] This flowchart shows the operation of an exemplary method for detecting attention deficit according to some embodiments of the present disclosure. [Figure 21] This flowchart shows the operation of an exemplary method for detecting attention deficit according to some embodiments of the present disclosure. [Modes for carrying out the invention]

[0023] The following describes in detail exemplary embodiments illustrated in the attached drawings. Throughout the drawings, the same reference numerals refer to the same elements.

[0024] According to one embodiment, the probability in Bayes' theorem can be transformed into Shannon information by applying a -log function to both sides of the equation. Thus, a Shannon metric called Transmitted Information (TIC) can be defined, which can reconcile the Shannon surprise and the Bayes surprise. Furthermore, TIC can explain how information flows (e.g., through which paths or networks) to increase or decrease uncertainty about an action in response to the behavioral relevance of a stimulus within the realm of behavioral choice or decision-making. Thus, TIC can be a link between belief and knowledge in cognitive modeling.

[0025] The decomposition of TIC can show that a single observation of a stimulus can generate two information quantities. One of these information quantities is called the bottom-up component and may be information related to evidence (e.g., a stimulus-limiting surprise that could trigger the occurrence of the event) and can contribute to the information flow with positive bits. The other is called the top-down component and may be information related to Bayesian likelihood and can contribute to the information flow with negative information bits. Therefore, if the absolute value of the latter is smaller than that of the former, this results in a net positive information measure (TIC > 0). Conversely, if the negative bits introduced by the top-down component are greater than the positive bits of the bottom-up component, this results in a net negative information measure (TIC < 0).

[0026] Furthermore, a conditional TIC can be defined, which can be derived from the conditional Bayes theorem, and thus an information flow conditioned on an internally maintained contextual representation (hereinafter referred to as "contextual TIC") or temporal context (hereinafter referred to as "episodic TIC"). TICs can be additive, and therefore, by adding the successive sensorimotor TICs, contextual TICs, and episodic TICs generated during evidence integration, the total number of uncertainty reductions can be calculated, and thus the uncertainty about action choices can be increased or decreased according to the sign of these TICs, and a given action can only be chosen when this uncertainty is converted into certainty.

[0027] According to the embodiments, insights obtained from TIC can be used to discover attention networks in cognitive disorders such as attention deficit hyperactivity disorder (ADHD). For example, subjects with cognitive disorders such as ADHD may need to increase prefrontal cortex (PFC) activity when dealing with exceptions to certain conditions (such as episodic task switching) or when filtering out unimportant stimuli, whereas healthy subjects may perform better when dealing with exceptions to conditions or filtering out unimportant stimuli by increasing activity in the dorsal-ventral attention network found by one or more embodiments of the Disclosure, without increasing PFC activity. This can explain why subjects with cognitive disorders (such as ADHD) may have more difficulty (compared to healthy subjects) maintaining focus, filtering out unimportant information, or dealing with exceptions. This is because subjects with cognitive disorders may need to use the PFC to filter out unimportant information or deal with exceptions, which can be extremely tiring compared to using the direct dorsal-ventral attention network without the PFC (as healthy subjects do). Therefore, this network can play a central role in bridging the gap between neurobiology and the symptoms of ADHD.

[0028] As described below, at least one of the activity of the PFC pathway or the activity of the dorsal-ventral attention network can be used as an indicator to detect whether a subject has a cognitive disorder such as ADHD.

[0029] Shannon Brain Model In prior art, log-probability neuron codes have been proposed for features (e.g., in images) that can take on specific values. Log-probability neuron codes were described in Rao, RPN, 2004, "Bayesian Computation in Recurrent Neural Circuits" (Neural Comput. 16, pp. 1-38) and Pouget, A. et al., 2013, "Probabilistic brains: knowns and unknowns" (Nat. Neurosci. 16, pp. 1170-1178). Nevertheless, how the transition from a probabilistic brain to a confident or one-time decision in the Bayesian brain occurs is not well understood in prior art.

[0030] According to the embodiment, event x i The Shannon divergence (or simply hereafter referred to as divergence) (which can be measured in bits) can be given as follows:

[0031] I(x i ) = -log2[p(x i )](bits)(Formula 1)

[0032] Similarly, Shannon entropy, which is also measured in bits, can be expressed as the sum of the information content of each event, weighted by the probability of occurrence.

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[0033] In one embodiment, it has been shown that expressing conventional probability theorems (such as Bayes' theorem) in terms of Shannon's information quantity can make it possible to understand how information is transmitted and how conventional negative information is generated. Averaging, such as the entropy expression in equation (2), can give interesting statistical implications, such as uncertainty about the content of a message, which Shannon was interested in for the purpose of transmission, but which can result in a loss of precision obtained from a given measurement. When an event occurs, probabilistic reasoning (performed in the brain) can transition to a deterministic environment given by Shannon's information quantity. As will be further shown below, this deterministic characteristic of Shannon's information quantity can serve as a bridge between belief and knowledge in cognitive modeling.

[0034] According to one embodiment, a Shannon metric can be derived that can harmonize stimulus-restricted surprises (such as the Shannon surprise) and enable the calculation of information gains (such as the Bayesian surprise) between prior and posterior beliefs. According to this embodiment, Bayes' theorem is expressed in terms of Shannon information by applying a -log function to both sides of Bayes' theorem. The effect of data x on the observer may, by Bayes' theorem, change the prior distribution P(θ) of the model θ to a posterior distribution P(θ|x). P(θ|x)=P(x|θ)P(θ) / P(x) (Equation 4) Here, P(x) is known as the evidence and P(x|θ) is known as the likelihood. Next, taking the -log2 function on both sides of equation (4), we can obtain the following equation. -log2P(θ|x)=-log2[P(x|θ)P(θ) / P(x)] (Equation 5)

[0035] By applying the properties of the log function, we can expand the right-hand side of equation (5) to obtain the following equation. -log2P(θ|x)=-log2P(x|θ)-log2P(θ)+log2P(x) (Equation 6)

[0036] Considering equation (1), equation (6) can be expressed in terms of the amount of information corresponding to each of the probabilities that appear in Bayes' theorem. I(θ|x)(bits)=I(θ)(bits)-I(x)(bits)+I(x|θ)(bits) (Equation 7)

[0037] In equation (7), each term can be the amount of information of a particular event, and the surprise that triggers the occurrence of the event can be represented in bits. By interpreting this last equation, it becomes possible to understand how bits of information introduced into the model space can be transmitted to each model, and concepts such as negative information can naturally arise. From equation (7), it can be deduced that the effect of data on the observer can be by changing the amount of information of a given model θ in the model space, that is, by changing (e.g., increasing or decreasing) the surprise that triggers the occurrence of the event. Therefore, the term I(θ|x) on the left side of equation (7) can be called the "posterior information of a single model θ", and the first term I(θ) on the right side of equation (7) can be called the "prior information of a single model θ".

[0038] The aspect of equation (7) is that the change in the amount of information of a single model θ in the model space, which can be induced by a single observation x, may include two amounts of information I(x) and I(x|θ) generated by the single observation (not just one amount of information as might be expected). Thus, in equation (7), -I(x)(bits)+I(x|θ)(bits) can represent the information transmitted to the single model θ by observation x. The amount of information transmitted (TIC) can be defined as follows: TIC(x→θ)(bits)=I(x)(bits)-I(x|θ)(bits) (Equation 8) Therefore, equation (7) can be expressed as follows: I(θ|x)(bits)=I(θ)(bits)-TIC(x→θ)(bits) (Equation 9)

[0039] In equation (8), the first term I(x) can be the Shannon information related to the evidence (e.g., the Shannon surprise), and positive bits can contribute to reducing the surprise of the posterior information I(θ|x). The second term I(x|θ) in equation (8) can also be the Shannon information, but since it is related to the Bayesian likelihood, it can also be Bayesian. This second term can contribute to increasing the surprise of the posterior information I(θ|x) with negative bits. For example, this can consider the surprise of the evidence I(x), and therefore only bits related to a single model θ can be transmitted to that model. Thus, TIC can make it possible to calculate the information gain (such as the Bayesian surprise) between prior and posterior beliefs. Since the Shannon information related to the Bayesian likelihood can be a link between the probabilistic and deterministic frameworks, this component of TIC can be a candidate for modeling Bayesian brain changes (further explained below).

[0040] As mentioned earlier, since TIC can describe information flow, it is not surprising that its sign can be of interest. In particular, if the negative bits of the information quantity in Bayesian likelihood I(x|θ) do not cancel out the positive bits of the information quantity in evidence I(x), then TIC is positive (i.e., there can be a positive net information transfer). Conversely, if the negative bits corresponding to I(x|θ) are greater than the bits in evidence I(x), then TIC is negative, and there can be a negative net information transfer. Thus, TIC can overcome the limitations of other information gain models.

[0041] According to one embodiment, a conditional TIC can be defined as follows:

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[0042] When equation (9) is used in the context of behavioral selection, it can express the information transmission that either triggers or does not trigger behavior a when stimulus s occurs, in relation to the so-called sensorimotor TIC(s→a). I(a|s) = I(a) - TIC(s→a) (Equation 11) Here, s can represent a stimulus and a can represent a behavior. Equation (11) shows that in behavioral selection, the TIC from stimulus s to behavior a may reduce (TIC>0) or increase (TIC<0) the uncertainty about behavior a. In the extreme case where TIC>0 cancels out the prior uncertainty I(a), there may be certainty that the behavior will occur, i.e., I(a|s)=0. In other words, if the uncertainty about a behavior is reduced to zero (via TIC), a decision to perform that behavior may be made. As mentioned earlier, TIC may also be the subtraction of two amounts of information. The addition and subtraction of amounts of information can be completed on a short time scale (e.g., the time scale on which brain attention acts), in contrast to the multiplication and division of probabilities that may occur when the brain (such as the Bayesian brain) cannot transition to an estimate (e.g., Shannon information). Therefore, it can be proposed that TIC may be a (deterministic) neural response that can reflect behavioral selection (or decision-making). This can bridge the gap between belief and knowledge. Since the net information transfer by TIC can be positive or negative, this gap can be increased or decreased, and in either case, it can provide precision to the previously estimated uncertainty.

[0043] The TIC in equation (11) can be decomposed as follows: I(a|s)=I(a)-TIC(s→a)=I(a)-I(s)+I(s|a) (Equation 12)

[0044] This decomposition of TIC may make it possible to identify and separate the bottom-up and top-down components in behavioral selection (or decision-making). This decomposition into bottom-up and top-down components may correspond to the attentional model of behavioral selection known as the Shannon brain model.

[0045] The first component of the TIC, I(s), can represent the amount of information related to the evidence (e.g., the stimulus-limiting Shannon surprise induced by stimulus s). Therefore, I(s) can be a bottom-up component of the attentional model that can generate positive information bits and thus contribute to a decrease in the information of the action (e.g., by reducing or even canceling out uncertainty). The second component of the TIC, I(s|a) (e.g., the amount of information related to Bayesian likelihood), can represent negative information generated to compute a Bayesian surprise that can increase the information of the action (e.g., by increasing uncertainty). Therefore, I(s|a) can be a top-down component of the attentional model and thus can properly process top-down attentional signals. In other words, a high top-down contribution may mean that no action is taken.

[0046] In a further embodiment, the information transfer that causes or does not cause action a when stimulus s occurs in context c can also be expressed in terms of TIC. The amount of information corresponding to the probability of causing action a given that stimulus s occurs in context c can be given as follows: I(a|s,c)=-log2[P(a|s,c)] (Equation 13)

[0047] Applying the conditional version of Bayes' theorem to the posterior probability of equation (13), we obtain the following equation:

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[0048] With respect to sensorimotor TIC, the components I(c|s) and I(c|a,s) of the contextual TIC in equation (17) can represent the bottom-up and top-down components of behavioral choice, respectively. It is possible for sensorimotor TIC to have a different sign from contextual TIC, and therefore, an informational stimulus with a positive sensorimotor TIC, when considered in a given context, can generate a negative contextual TIC that compensates for the positive sensorimotor TIC, and the stimulus does not provide information about that context.

[0049] In an additional embodiment, the information transfer that occurs in context c and episode (such as temporal context) e, and that either causes or does not cause behavior a, can be calculated with respect to the TIC. In this case, the amount of information can be given by the following formula: I(a|s,c,e)=-log2[P(a|s,c,e)] (Equation 18)

[0050] Applying the conditional version of Bayes' theorem to the posterior probability of equation (18), we obtain the following equation:

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[0051] The first term of equation (20) may be given by equations (13) and (16). From equation (10), the second term may be episodic conditional TIC:TIC(c→a|s,c), which can represent the information bits transmitted to the behavior when the stimulus occurs in a given context c and episode e. Therefore, the amount of information can be expressed by the following equation. I(a|s,c,e)=I(a)-TIC(s→a)-TIC(c→a|s)-TIC(e→a|s,c) (Equation 21)

[0052] Furthermore, the sensorimotor TIC, context-conditioned TIC, and episodic-conditioned TIC in equation (21) can be decomposed into two components.

[0053] I(a|s,c,e)=I(a)-TIC(s→a)-TIC(c→a|s)-[I(e|s,c)-I(e|a,s,c)]=I(a)-I(s)+I(s|a)-I(c|s)+I(c|a,s)-I(e|s,c)+I(e|a,s,c) (Equation 22)

[0054] The components I(e|s,c) and I(e|a,s,c) of the episodic conditioned TIC in equation (22) can represent the bottom-up and top-down components of behavioral choice, respectively. Following the inference for the contextual case, if the episodic conditioned TIC in equation (22) is positive and compensates for the negativity of the contextual conditioned TIC, then a stimulus that would otherwise provide no information in a given context may become informational.

[0055] From equation (22), it can be seen that the uncertainty about the action when a stimulus occurs in a given context and episode can increase or decrease depending on the bottom-up and top-down contributions of the amount of information (e.g., log probabilities that can be added and subtracted). This will be explained further below. In equations (21) and (22), I(a) can represent the bits of uncertainty about the action before the stimulus appears, and I(a|s,c,e) is the uncertainty remaining after the stimulus appears and the subject has considered the context c and episode e (if applicable), depending on the sign of each TIC (e.g., it can be higher or lower than I(a)). The first component of each of the three TICs (I(s), I(c|s), I(e|s,c)) can correspond to the bottom-up contribution of stimulus s and can always contribute to reducing the uncertainty of the action (e.g., pressing a button). These bottom-up components appear with a negative sign in Equation 22, meaning they contribute to reducing the bit of uncertainty about the action (increasing the probability of occurrence). The bottom-up components can always appear with a positive sign in the TIC equation, because they can be considered positive information components (e.g., Shannon surprises). The opposite is true for the second components of each of the three TICs (I(s|a), I(c|a,s), I(e|a,s,c), the top-down components). The top-down components appear with a positive sign in Equation 22, meaning they can contribute to increasing uncertainty about the action. The top-down components appear with a negative sign in the TIC, and therefore can be negative information components. Consequently, if their absolute value is greater than their respective Shannon surprises (I(s), I(c|s), I(e|s,c)), the corresponding TIC can be negative, and its net contribution is an increase in uncertainty about the action (a decrease in the probability of occurrence). These second components of TIC can be considered top-down components because they can contribute to behaviorally relating a stimulus (e.g., when low or zero) or non-behaviorally relating it (e.g., when high), depending on the context or episode.For example, subjects with cognitive disorders such as ADHD may have difficulty distinguishing between behaviorally relevant and non-behaviorally relevant stimuli, and therefore, the top-down component of the TIC may be processed differently by these subjects (compared to healthy subjects).

[0056] While the roles of the dorsal and ventral attention networks are known in conventional technologies, how these two networks communicate with each other remains a subject of considerable debate.

[0057] Once a task is explained to a subject, it may be stored in the subject's memory (i.e., brain). The dorsal attentional network then transmits one or more top-down signals, which may correspond to non-target, distracting, or other stimuli different from the target, to the ventral attentional network via the middle frontal gyrus (MFG). These top-down signals are then stored in a brain region called the temporoparietal junction (TPJ). The TPJ may include the posterior part of the superior temporal sulcus (STS), the posterior part of the superior temporal gyrus (STG), and the inferior parietal lobule (IPL) (Corbetta et al., 2008, The Reorienting System of the Human Brain: From Environment to Theory of Mind, Neuron 58, pp. 306-324). The inferior parietal lobule includes the angular gyrus (AG) and the supramarginal gyrus (SMG).

[0058] Below, with reference to Figure 1, we will describe the information flow of positive and negative bits that may occur between the dorsal attentional network (DAN) and the ventral attentional network (VAN) (e.g., within the Shannon brain model). Furthermore, as previously mentioned, the top-down contribution in the Shannon brain model may be given by the amount of information related to Bayesian likelihood, which can generate negative information bits that can cancel out bits generated by evidence (or surprise). Thus, TPJs (e.g., including IPL and STG) can function as memory for negative information bits. This is consistent with the role of the ventral attentional network (VAN), which can only be activated when a stimulus is behaviorally relevant in a given context or episode (e.g., one or more targets or accidental interfering stimuli) (e.g., when the stimulus is informational, such as when TIC > 0). Next, the amount of information associated with Bayesian likelihood can be low or zero, and therefore, the bottom-up bits that can be transmitted to the dorsal attentional network (DAN) by the stimulus (e.g., generated in the brain's visual cortex) cannot be canceled out by the top-down negative bits originating from the ventral attentional network, and the stimulus may be preferred and reinforced in one or more cerebral cortical visual areas. On the other hand, if the stimulus provides no information (TIC ≤ 0), the absolute value of the amount of information associated with Bayesian likelihood can be quite high, and therefore, the ventral attentional network (VAN) can send a large amount of negative bits to the dorsal attentional network (DAN) that can cancel out the positive bottom-up bits of the stimulus. As a result, non-behaviorally relevant stimuli (non-targeted, distracting stimuli, etc.) may lose priority and, consequently, be rendered ineffective.

[0059] This may be applicable to the sensorimotor TIC, contextual TIC, and episodic TIC frameworks (for example, by the linearity of equations 21 and 22, which consider the contributions of these TICs). Thus, the TPJ can function as a memory for negative information bits of sensorimotor, contextual, and episodic events given by I(s|a), I(c|a,s), and I(e|a,s,c) (such as the top-down contributions of TICs). In the aforementioned Shannon brain model, these three information quantities related to Bayesian likelihood can be subtracted in priority-mapped brain regions by the corresponding bottom-up contributions of the TICs, i.e., I(s), I(c|s), and I(e|s,c). Thus, the sensorimotor TIC, contextual TIC, and episodic TIC can be generated and subtracted from the prior uncertainty of the action I(a) to derive the posterior uncertainty I(a|s,c,e). As mentioned above, different situations may arise depending on the sign of these TICs. For example, if the contextual TIC is negative, the stimulus does not provide information in its context, but the episodic TIC is positive, the episodic TIC cancels out the contextual TIC, and the stimulus provides information in a new temporal context. This is illustrated in Figure 1.

[0060] As shown in Figure 1, the TPJ can function as a memory for negative information bits of sensorimotor, context, and episode, given by I(s|a), I(c|a,s), and I(e|a,s,c), respectively. These three components can be subtracted from the bottom-up contributions I(s), I(c|s), and I(e|s,c) of sensorimotor, context, and episode in one or more of two priority-mapped brain regions (e.g., the anterior inferior temporal cortex (AIT) and / or the intraparietal sulcus (IPS) / superior parietal lobule (SPL)). Thus, sensorimotor TIC, context TIC, and episode TIC can be generated and subtracted from the prior uncertainty of behavior I(a) to derive the posterior uncertainty I(a|s,c,e). The dorsal attention network (DAN) and ventral attention network (VAN) can communicate through the MFG with the involvement of the spleness network (SN) (not shown in Figure 1).

[0061] The ventral attention network (VAN) can transmit signals to the dorsal attention network (DAN) not only when activated (e.g., one or more bottom-up signals for reorientation), but also when deactivated (e.g., negative bits of one or more top-down signals). Therefore, information flow from the ventral attention network (VAN) to the dorsal attention network (DAN) is possible not only when activated, but also when deactivated. This can be counterintuitive because deactivation and information flow are seemingly opposing concepts, but on the other hand, it can help understand the equally counterintuitive concept of negative information, as it can be the type of information one would expect to come from a deactivated source.

[0062] Furthermore, in prior art, a color bias network (CBN) (within the ventral visual pathway (VVP)) is known, which may include the bilateral lingual gyrus, fusiform gyrus, right inferior occipital gyrus, and one or more adjacent occipitotemporal parietal regions. The posterior region of this network is passive and can compute low-level color information (such as color perception), while the anterior region is active and can encode more complex functions (such as color knowledge), such as combining shape and color for the purpose of object recognition, storing typical colors of given objects in memory and associating them, or storing the color and shape of a given behaviorally related stimulus in memory. Depending on the nature of the relationship between this network and memory, this relationship can be associated with hippocampal brain structure. These associations are known as object-color memory. Shape and color can be processed separately through much of the ventral visual pathway (VVP), and in fact, the lateral occipital (LO) network of shape bias is known. Both networks converge in the most anterior color bias region (color knowledge) (e.g., the anterior inferior temporal cortex (AIT)), where shape and color can be combined for the purpose of object recognition.

[0063] Similar to other networks such as the dorsal attention network (DAN) and ventral attention network (VAN), the cognitive functions of the color bias network (CBN) (color perception, color names, color knowledge) can be associated through one or more bottom-up (e.g., feedforward) posterior-to-anterior pathways, and can also be top-down modulated (e.g., feedback) through one or more anterior-to-posterior pathways. Thus, irrelevant stimuli can be suppressed in the early visual cortex by top-down modulation based on the encoding of object color knowledge. Top-down modulation of the color bias network (CBN) and shape bias network (SBN), as previously described for the dorsal attention network (DAN) and ventral attention network (VAN), can arise from filtering signals stored in the TPJ of the ventral attention network (VAN), which originate in the prefrontal cortex (PFC) and whose activation can be restricted to relevant object-color knowledge. The ventral attention network (VAN) can then transmit this top-down signal to the AIT, thereby refining the representation of priority object-color knowledge by reinforcing or suppressing the influence of bottom-up inputs in the early visual cortex.

[0064] Again, the sensorimotor TIC, contextual TIC, and episodic TIC frameworks, along with color bias networks (CBNs) and shape bias networks (SBNs), can find a natural place in this top-down modulation role of the ventral attention network (VAN). The TPJ can store the negative bits of object-color knowledge corresponding to the sensorimotor information, contextual information, and episodic information related to the Bayesian likelihood of equation (22), namely I(s|a), I(c|a,s), and I(e|a,s,c), respectively. Then, the representation of priority object-color knowledge can be adjusted by subtracting each of these information quantities from the respective bottom-up components of the AIT, I(s), I(c|s), and I(e|s,c), and calculating the corresponding sensorimotor TIC, contextual TIC, and episodic TIC of equation (21), as shown in Figure 1. If the color of a stimulus is considered to be a temporal context or episode that may be more or less behaviorally relevant, then the sign of the episode TIC will contribute to an increase or decrease in uncertainty about the behavior. Therefore, the ventral attention network (VAN) may be top-down modulated (e.g., inhibited or reoriented) not only the dorsal attention network (DAN) but also the color bias network (CBN) and the shape bias network (SBN).

[0065] Anterior-posterior communication may exist between the dorsal attention network (DAN) and the ventral attention network (VAN). In particular, the frontal cortex (e.g., the prefrontal cortex (PFC)) may be a candidate for this interaction. For example, the inferior frontal gyrus (IFG) and middle frontal gyrus (MFG) may be candidates for this interaction. Furthermore, this interaction may be driven by a different network, the spleness network (SN, not shown in Figure 1), based on the sustained activation of two of its nodes, the anterior cingulate cortex (ACC) and the anterior insula (AI), during top-down filtering or reorientation. The same interaction may also apply to the shape bias network (SBN) and color bias network (CBN) and the ventral attention network (VAN), i.e., their communication may be driven by the SN through the IFG and MFG. During cognitive tasks, the SN can play two roles: "set maintenance" and monitoring decisions made to monitor task objectives. Therefore, the SN may be involved in anterior-posterior interactions between the ventral attention network (VAN) and the other networks.

[0066] As shown in Figure 2, in addition to anterior-posterior transmission, (direct) dorsal-ventral transmission (DVAN) can coexist with the anterior-posterior transmission described in the context of Figure 1. In the dorsal-ventral attention network (DVAN), the TPJ of the ventral attention network (VAN) can top-down modulate (e.g., filter or reorient) one of the dorsal attention network (DAN), ventral shape network (VVP), and color bias network (CBN) to adjust sensory input without the involvement of the PFC region. This dorsal-ventral attention network (DVAN) is shown in Figure 2. In particular, Figure 2 shows the coexistence of the dorsal-ventral pathway (DVAN) in addition to the anterior-posterior transmission described above. This dorsal-ventral attention network (DVAN) may include the occipital visual cortex, the VVP's shape bias network (SBN) and color bias network (CBN), two priority maps (IPS / SPL and AIT), and one or more of the posterior parts of the dorsal and ventral attention networks. The TPJ of the ventral attention network can also be a node in this network and can communicate dorsally and ventrally with the IPS priority map and AIT priority map, respectively. This network can be a rapid track for addressing top-down violations (or filtering of irrelevant stimuli) and can compute sensorimotor TIC, contextual TIC, and episodic TIC without the involvement of additional PFC resources. In particular, by using direct links between IPS / SPL and TPJ, and / or between TPJ and AIT (both shown in the DVAN in Figure 2), information can be exchanged between the dorsal attention network (DAN) and the ventral attention network (VAN) without the involvement of the MFG or IFG (shown as dashed lines in Figure 2). Therefore, these direct links can ensure that the brain can process information without the involvement of the prefrontal cortex.

[0067] Experimental results A sample of 28 infants, consisting of 14 ADHD participants (age 10 years 5 months, standard deviation = 1 year 11 months, female = 35.7%) and 14 control participants (age 10 years 7 months, standard deviation = 1 year 8 months, female = 35.7%), was recruited for a magnetoencephalography (MEG) study. Neurologists diagnosed and evaluated the ADHD participants and issued neurological reports including an ADHD diagnosis. This study was approved by the Hospital Clinico San Carlos Review Board in Madrid, and all participants and their legal guardians signed informed consent before MEG recording.

[0068] In one example, MEG data including brain signals were measured using a 306-channel (102 magnetometers and 204 planar gradiometers) whole-head MEG Elekta Neuromag system. In another example, brain signals were measured using an online anti-aliased bandpass filter from 0.1 to 330 Hz and a sampling rate of 1000 Hz. In one example, the participant's head shape was acquired using a 3D Fastrak digitizer (Polhemus, Colchester, Vermont), in addition to three reference points (nasal root and left and right preauricular points). In one example, four head position indicator (HPI) coils were recorded on the participant's scalp (forehead and mastoid process). In one example, blinks and heart rate were recorded using two sets of bipolar electrodes, respectively.

[0069] Data preprocessing was performed in several steps. In one example, to remove external noise from the raw data, a temporal extension of the spatial signal separation (tSSS) method was applied using a window length of 10 seconds and a correlation threshold of 0.90 as input parameters. In another example, automated detection of eye, heart, and muscle artifacts was performed and validated by MEG experts. Finally, in one example, independent component analysis was performed to remove blinking and cardiac activity. In another example, the data was segmented into 1-second trials (300-millisecond baseline and 700-millisecond task-related data) according to the task event, and trials that were found to contain artifacts were discarded from subsequent analysis.

[0070] In one example, a uniform grid with 1 cm spacing between source locations within the participant's cranial cavity was created using a template from the Montreal Neurological Institute (MNI), placing 2459 source locations within the participant's cranial cavity. In one example, these source locations were labeled according to the Automated Anatomical Labeling Atlas (AAL), and only 1188 source locations labeled as one of 76 cortical regions were retained. In one example, this surface was linearly transformed to match the participant's position in the MEG scanner, using the head shape as an aid, along with the source model of the target space. In one example, the internal cranial interface was used in combination with the sensor definitions provided by the aforementioned source model and system to solve the forward model, and the read field was constructed based on the modified spherical solution. In one example, source-level activity was reconstructed using the mean of the single-trial covariance matrix across the entire segment, with a linear-constrained minimum variance (LCMV) beamformer as the inverse method, and normalized using the Tikhonov method and the lambda coefficient of the 10% mean channel power. To compute a common beamformer filter for magnetometers and gradiometers, in one example, the data and read field matrices were normalized channel by channel, thus allowing comparison of signal amplitudes for different channel types. In a further example, a three-dimensional time series was projected onto the spatial principal components to obtain a single source-level time series for each source location.

[0071] In one example, to calculate phase synchronization, source-level activity was calculated separately for each of the conventional bands: theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), high beta (20–30 Hz), and low gamma (30–45 Hz). In one example, band-specific data was filtered using an 1800th-order FIR filter constructed with a Hamming window and applied to a two-pass procedure to remove any possible phase distortion. In another example, to avoid edge effects, 1 second of real data was used as padding on each side to filter the data. It can be seen that a separate time series is obtained for each source position in the participant's cranial cavity.

[0072] In one example, correlation values ​​(such as functional coupling (FC) analysis) were calculated for pairs of time series i and j (each time series corresponding to its respective source position). In another example, FC analysis was calculated using phase-lock values ​​(PLV, for example, based on the phase-lock hypothesis). PLV may be based on the distribution of the instantaneous phase difference between two time-dependent signals (such as a pair of time series).

number

number

number

[0073] PLV can take values from 0 to 1, where 0 indicates absolute phase independence (such as low or no correlation), and 1 indicates absolute phase dependence (such as high correlation). In one example, the instantaneous phase of a signal can be estimated using the Hilbert analysis signal after band-pass filtering. To avoid edge effects for the extraction of the instantaneous phase, according to one example, the Hilbert analysis signal can be calculated using 1 second of real data padded on each side of the trial. In one example, within the time window of 50 to 450 milliseconds after the stimulus, PLV i,j was calculated for each frequency band (theta 4 - 8 Hz, alpha 8 - 12 Hz, low beta 12 - 20 Hz, high beta 20 - 30 Hz, and low gamma 30 - 45 Hz).

[0074] In one example, PLV i,j was calculated independently for each task (e.g., Task 1 and Task 2 described further below) and for each condition (Go, No - Go, and red E vowel, also described further below). In one example, PLV i,j was calculated for each pair of source positions i and j, generating an 1188×1188 FC matrix (i.e., i = 1…1188, j = 1…1188). In another example, the regional - level PLV i,j was calculated as the average PLV of all cortical sources contained within each of the 76 cortical regions of interest (ROIs) based on, for example, the AAL atlas, obtaining a 76×76 whole - brain matrix (i.e., i = 1…76, j = 1…76). It is clear that other sizes of the matrix PLV i,j are possible. In another example, PLV i,j was calculated for specific cortical networks such as the salience network (SN) and the whole, right, and left central executive networks (ECN). In this case, the PLV pairs of the ROIs that make up each network were extracted, obtaining subsequent matrices of 8×8 for the SN and 14×14 (7×7 ROIs for right / left) for the ECN.

[0075] For the whole-brain analysis example (the case of i=1…1188, j=1…1188 as described above), a permutation-based t-test was used to compare the PLV values ​​of each pair of ROIs. Independent sample analysis using t-tests can be used for comparisons within each functional network and between groups for each condition. For within-group comparisons, relevant sample analysis can be performed between pairs of conditions of interest, as well as between similar conditions of tasks both within each group and within each functional network. In the example, the resulting p-values ​​can be corrected for multiple comparisons (number of pair comparisons and number of frequency bands) with a false positive rate (FDR) of 0.1 to obtain an FDR-corrected alpha threshold for each analysis. In one example, a p-value below the threshold can be reported as significant.

[0076] In one example, in a Go / No-Go task (hereinafter referred to as "Task 1"), one or more stimuli, such as one or more letters, are presented to the participant on a task-presenting device, and the participant is instructed to press a button when a vowel appears on the device and not to press a button when a consonant appears. In this example, the letters may have different colors (red, green, blue, white, etc.). Furthermore, an exception is added to the task, so that the participant is instructed not to press the button when the letter is red. After completing this task, the participant can start a new task (hereinafter referred to as "Task 2"), which may include a temporal context or episode in which the participant should press the button when a red vowel e appears (an exception to the exception, etc.). This second task can measure cognitive flexibility and the ability to modify patterns learned during the first task.

[0077] [Table 1] [Table 2]

[0078] Furthermore, in this example, stimuli can be generated in the following four uniformly distributed groups: GoA = vowels other than red, NoGoA = consonants, GoB = the red letter "e", and NoGoB = red vowels other than "e". The probabilities derived from the uniformly distributed stimulus groups yield the following prior probabilities for the go and no-go conditions of each task: Task 1: Go condition = GoA → P(Go) = 1 / 4, No-Go condition = GoB + NoGoA + NoGoB → P(NoGo) = 3 / 4. Task 2: Go condition = GoA + GoB → P(Go) = 1 / 2, No-Go condition = NoGoA + NoGoB → P(NoGo) = 1 / 2. Figure 3 shows the conditional probabilities of each associated characteristic according to the model described above. When a particular probability tends to be zero, it takes 10 to compute the associated information to avoid infinity. -5 We can assume a value for this.

[0079] In Task 1, which involves presenting consonants to participants, there may be no relevant context, and therefore, applying equation (12), we obtain the following equation:

number

[0080] Furthermore, under the conditions of Task 1, which involves presenting participants with vowels other than red, applying equation (17) to a context of colors other than red yields the following equation.

number

[0081] Furthermore, under the conditions of Task 1, which involves presenting participants with vowels other than those in red, applying equation (17) to the context of red yields the following equation.

number

[0082] Furthermore, in the Task 2 episode condition, the participant should press the button when the red vowel "e" is presented, and in this case, the same context as the last part (the vowel is red) may occur, but top-down control may not be generated. Applying equation (22), the values ​​of each preceding component may be the same as in the previous case, except for I(a), which may differ in Task 2 due to the increase in pulsation frequency caused by the new episode condition, but two new terms may appear to take into account the episode component information.

number

[0083] Again, uncertainty about the action can be reduced to zero, so the result is the decision to press the button. Therefore, in the case of this episode, I(e|s,c)>>I(e|a,s,c)⇒TIC(e→a|s,c)>0, that is, there can be positive information transfer from the evidence of the appearance of the red vowel e to the action of pressing the button. In this case, the contextual TIC can be negative, but it can be compensated for by a positive episode TIC that is greater than the contextual TIC. There are several contributions to Equation 27.

[0084] Sensorimotor TIC: The subject sees a vowel, i.e., Shannon Surprise I(s), but its probability of occurrence is 3 / 4, i.e., 0.42 bits of positive information, which instantaneously reduces the uncertainty to 0.58 bits. In this sensorimotor stage, the negative information generated by the other component I(s|a) of the TIC is null and has no effect. In this case, the subject may have been informed of the vowel's probability of occurrence beforehand, or may have learned it during the performance of the task.

[0085] Contextual TIC: The subject can consider the context, in which case they can consider the color of the stimulus by the bottom-up component I(c|s) of the contextually conditioned TIC. This can again be a Shannon surprise of 0.58 bits (probability of red vowel 2 / 3). Next, the subject can consider the top-down component (e.g., I(c|a,s)), which can introduce 1 bit of negative information, increasing the uncertainty about pressing the button (e.g., decreasing the probability of doing so).

[0086] Episode TIC: Up to this point, the initial 1-bit uncertainty was reduced by exactly 1 bit (0.42 + 0.58) by the two Shannon surprises I(s) and I(c|s). However, the contextual TIC introduced 1 bit of negative information through the top-down component I(c|a,s), increasing the uncertainty back to 1 bit. Thus, when the subject processes the stimulus as being a vowel and that it is red, the subject has 1 bit of uncertainty. If the stimulus is "e", the subject may wait to process because this could be the episodic condition that should induce pressing the button in Task 2. The episodic TIC is 1 bit, and episode TIC(e=red e→a|s,c) = I(e=red E|s=vowel,c=red) - I(e=red E|a,s=vowel,c=red) = 1 - 0 = 1 bit. Thus, the episodic TIC can be 1 bit as expected, and subtracting it in Equation 27 reduces the uncertainty to 0 bits, allowing the button to pulsate.

[0087] MEG data measured from participants may include brain signals in the alpha, beta, and theta bands. The experimental results are described below in the context of Figures 4–12. On the left side of each of these figures, diagrams of the posterior, superior, left, and right sides of the brain are shown, along with links indicating significantly different PLV values. On the right side of each of these figures, pie plots show schematic diagrams of significant links (corresponding to the plots on the left). Labels are abbreviations for brain regions, following the automated anatomical labeling atlas shown at the end of the explanation.

[0088] In the first example, the PLV of the healthy state (HC) group (healthy participants, etc.) performing Task 1 under the No-Go condition (consonant or red vowel) can be compared with that of the Go condition (no red vowel) in the alpha band (8-12 Hz). Figure 4 shows links with significantly different PLV values ​​in the first example. In the No-Go condition, inactivation of the occipitotemporal parietal cortex and ventral frontal cortex can be observed. In particular, Figure 4 shows lines indicating links with significantly different PLV values ​​in the comparison between the No-Go and Go conditions of the HC group performing Task 1. On the left, diagrams of the posterior, superior, left, and right sides of the brain are shown. On the right, a pie plot shows a schematic diagram of significant links. The lines indicate a decrease in PLV values ​​in the No-Go condition compared to the Go condition. A link with significantly different PLV values ​​can be seen between the left supramarginal gyrus (ISMG, part of the temporoparietal junction) and the left inferior temporal gyrus (IITG, part of the VVP). This is consistent with the aforementioned Shannon brain model, in which the ventral attentional network (VAN) can transmit such top-down signals to the VVP network, thereby modulating the representation of priority object knowledge by reinforcing or suppressing the influence of bottom-up inputs from the early visual cortex.

[0089] In this case, under the Go condition (vowels other than red), equation (25) shows that the two information quantities related to Bayesian likelihood, I(s=vowel|a) and I(c=non-red|a,s=vowel), are zero. Conversely, under the No-Go condition, if the stimulus is a consonant, equation (24) shows that the information quantity I(s=consonant|a) related to Bayesian likelihood is large, resulting in a negative sensorimotor TIC. If the stimulus is a red vowel, equation (26) shows that the non-null value of I(c=red|a,s=vowel) indicates a negative contextual TIC. Under the No-Go condition, these negative information quantities related to Bayesian likelihood can deactivate the ventral attention network. Furthermore, when these negative top-down bits are subtracted from the bottom-up bits corresponding to the Shannon surprise of the stimulus, a negative TIC is generated in the anterior inferior temporal lobe, which can deactivate the VVP and the visual cortex of the occipital cortex. This can be observed in Figure 4, and in addition to the link between the left suprammarginal gyrus of the TPJ and the left inferior temporal cortex of the VVP, there is a link between the temporal and occipital regions, all of which exhibit the aforementioned inactivation in these areas under the No-Go condition, which can be explained by negative sensorimotor TIC when the stimulus is a consonant, or by negative contextual TIC when the stimulus is a vowel. Furthermore, the link between the IFG and the middle temporal gyrus can indicate that the interaction between the ventral attention network and the shape network is driven by SN.

[0090] In the second example, the alpha band (8-12 Hz) PLV of the HC group performing Task 1 in the No-Go condition when the stimulus was the red vowel e is compared to Task 2 in the Go episode condition (red vowel e). Figure 5 shows links with significantly different PLV values ​​for this second example. In the No-Go condition of Task 1 (red vowel e), deactivation of the occipitotemporal parietal cortex can be observed. In particular, Figure 5 shows lines indicating links with significantly different PLV values ​​in the No-Go condition of Task 1 (red vowel e) compared to the Episode Go condition of Task 2 (red vowel e) in the HC group. On the left, diagrams of the posterior, superior, left, and right sides of the brain are shown. On the right, a pie plot shows a schematic diagram of significant links. The lines indicate a decrease in PLV values ​​in the No-Go condition of Task 1 (red vowel e) compared to the Episode Go condition of Task 2 (red vowel e). For completeness, links with significantly different PLV values ​​for this second example are repeated in the following table, as shown in Figure 5.

[0091] [Table 3-1] [Table 3-2] [Table 3-3]

[0092] The PLV results are consistent with the Shannon brain model described above. In the No-Go condition of Task 1, the amount of information associated with Bayesian likelihood I(c=red|a,s=vowel) can generate negative bits that can be stored in the TPJ of the ventral attention network, and then transmitted to the priority map region to cancel out the bottom-up bits from the input signal. In the case of the red vowel, the Shannon brain model described above predicts that the negative information bits corresponding to term I(c=red|a,s=vowel) are stored in the TPJ of the ventral attention network, and that when the red vowel is presented, these negative information bits can be transmitted to suppress the color bias network. This can be observed in Figure 5. There are links with significantly different PLV values ​​between regions of the TPJ (left supramarginal gyrus, bilateral angular gyrus, etc.) and regions of the color bias network such as the bilateral lingual gyrus, fusiform gyrus, and right inferior occipital gyrus.

[0093] Furthermore, there is a link between the superior parietal gyrus (a region belonging to the dorsal attention network where the priority map is identified) and the occipital visual cortex, also belonging to the dorsal attention network. These links support the idea that top-down modulation of the occipital visual cortex by higher-order regions also occurs in the dorsal attention network. In Task 2, as shown in equation (32), in contrast to the deactivation of the color bias network in Task 1, uncertainty about behavior a can be completely counteracted by the episode TIC, and therefore the color bias network can be strengthened. Thus, the difference in PLV values ​​that can be observed in Figure 5 may be due not only to the deactivation of the color bias network in Task 1, but also to the strengthening of this network in Task 2. Furthermore, Figure 5 shows that the reconstruction of the mental ability to switch tasks, implied by the different processing of the red vowel e in Task 1 and Task 2, can be achieved without additional PFC increments. These results support the hypothesized dorsal-ventral attention network shown in Figure 2, in which a top-down TPJ in the ventral attention network can modulate (e.g., filter or reorient) not only the dorsal attention network but also the ventral shape network and color network, thereby adjusting sensory input, without the involvement of the PFC region.

[0094] In the third example shown in Figure 6, the alpha band (8-12 Hz) PLV of the ADHD group performing Task 1 in the No-Go condition when the stimulus is the red vowel e is compared to Task 2 in the Go episode condition (red vowel e). Figure 6 shows the central executive network (CEN) of the ADHD group (e.g., participants diagnosed with ADHD). Links with significantly different PLV values ​​in Task 1's No-Go condition (red vowel e) compared to Task 2's Episode Go condition (red vowel e) are indicated by lines. On the left, diagrams of the posterior, superior, left, and right sides of the brain are shown. On the right, a pie plot shows a schematic of significant links. Lines indicate a decrease in PLV values ​​in Task 1's No-Go condition (red vowel e) compared to Task 2's Episode Go condition (red vowel e). In Figure 6, links with significantly different FC values ​​can be seen. In contrast to the HC group, the ADHD group can reconstruct the mental ability to switch tasks, implied by the different processing of the red vowel 'e' in Task 1 and Task 2, by involving an additional PFC increment. For example, the PFC regions involved are the IFG (e.g., the left inferior frontal triangular gyrus and opercular gyrus) and the right middle frontal gyrus, which show communication between the dorsal and ventral attentional networks via the SN. There are three links between these three regions of the PFC and the superior parietal gyrus (e.g., the left superior parietal gyrus (ISPG)), a node of the dorsal attentional network. Furthermore, a direct dorsal-ventral transmission can be observed in the link between the left superior parietal gyrus and the right angular gyrus of the ventral attentional network.

[0095] Regarding Figure 2, in the dorsal-ventral attention network (DVAN) described above, the ventral attention network (VAN) can play a role in transmitting signals to the dorsal attention network (DAN) and VVP, respectively, in the dorsal and ventral directions. This can represent a direct pathway for filtering irrelevant stimuli or reorienting behaviorally relevant stimuli, and this pathway is far more efficient than anterior-posterior pathways (such as the pathway through the IFG and MFG described in relation to Figure 1) that involve the PFC and can be driven by the SN through the PFC. Therefore, subjects performing Task 1 and Task 2 may demonstrate the inefficiency of subjects with cognitive disorders (such as ADHD). This is because such subjects may need to increase activity in the PFC pathway when dealing with task switching (e.g., changes in temporal context or episode, such as exceptions to conditions). The HC group can perform task switching by increasing activity in the dorsal-ventral network without increasing activity in the PFC pathway. Figure 1 can be said to represent the brain connectivity in ADHD, and Figure 2 can be said to represent the brain connectivity in HC, both relating to the reorganization of mental abilities through task switching.

[0096] In the fourth example, we compare PLV values ​​in the Go condition for both Task 1 and Task 2. In the Go condition for Task 1 (non-red vowels), the connectivity results show that the PLV values ​​of the HC group are increased in the alpha band (8-12 Hz) compared to the ADHD group. Therefore, non-red vowels, which are behaviorally relevant stimuli, can be top-down modulated, while irrelevant stimuli (consonants and red vowels) can be suppressed at the same time. Figure 7 shows the links in the central executive network where the HC group has significantly different PLV values ​​compared to the ADHD group in the Go condition for Task 1 (non-red vowels) in the alpha band (8-12 Hz). On the left, diagrams of the posterior, superior, left, and right sides of the brain are shown. On the right, the pie plot shows a schematic diagram of significant links. The line shows that the PLV values ​​of the HC group are increased compared to the ADHD group in the Go condition for Task 1 (non-red vowels). In particular, Figure 7 shows links belonging to the central executive network (CEN), which includes the dorsal attention network and the ventral attention network. Increased PLV values ​​were observed in HC subjects compared to the ADHD group in the links between the dorsal attention network (e.g., superior parietal gyrus), the ventral attention network (e.g., bilateral angular gyrus), the IFG (intrafrontal glenoid gyrus, inferior frontal triangular gyrus, etc.) and the middle frontal gyrus. These links indicate interaction between the dorsal and ventral attention networks via the SN, which is the middle frontal gyrus, the link between the two. This may mean that the ventral attention network in the HC group is sending stronger bottom-up reorientation signals to the dorsal attention network than in the ADHD group.

[0097] Furthermore, Figure 8 shows links with significantly different (i.e., increased) PLV values ​​between the HC group and the ADHD group in the alpha band (8–12 Hz) under the Go condition (vowels other than red). The illustrated network is the splenic network (SN), also known as the cingulate cortex network. The increased PLV values ​​in HC compared to the ADHD group can be observed in links between most of the nodes of the cingulate cortex network (e.g., bilateral insular cortex, bilateral inferior frontal triangular gyrus, bilateral infraorbital frontal gyrus, left infraoperifrontal gyrus, and right anterior cingulate cortex). The superior cognitive performance of the HC group in Task 1 compared to the ADHD group can also be explained by this increase in PLV values ​​in the SN, which can provide stable "set maintenance" throughout the task time and play a role in monitoring the achievement of the desired goal. In particular, Figure 8 shows links in the splenic network where the HC group has significantly different PLV values ​​compared to the ADHD group in the alpha band (8–12 Hz) under the Go condition (vowels other than red) of Task 1. On the left, diagrams of the posterior, superior, left, and right sides of the brain are shown. On the right, a pie plot shows a schematic of significant links. The line indicates that the PLV value was increased in the HC group compared to the ADHD group in the Go condition of Task 1 (vowels other than red).

[0098] In the fifth example, we compare the links of the HC group, which had significantly reduced PLV values ​​in the beta band (20-30 Hz) in the Task 2 Go condition (non-red vowels and the red vowel e), with those of the ADHD group. In the Task 2 Go condition, the vowel e can be a top-down violation of the episode, which may require the behavioral flexibility gained by the reduction in the beta band. Therefore, the higher beta band synchronization of the ADHD group can explain the performance deficit of this group in Task 2, which is shown by lower accuracy rates for both non-red vowels (89.93% for ADHD vs. 95.00% for HC, p=0.017) and the red vowel e (82.53% for ADHD vs. 90.27% for HC, p=0.022) (86.23% for ADHD vs. 92.63% for HC, p=0.009). Figure 9 shows the links in the central executive network (CEN) where the HC group had significantly different PLV values ​​compared to the ADHD group in the beta band (20-30 Hz) during the Go condition of Task 2. The left side shows diagrams of the posterior, superior, left, and right sides of the brain. On the right side, a pie plot shows a schematic of the significant links. The line indicates a decrease in PLV values ​​in the HC group compared to the ADHD group during the Go condition of Task 2. From the PLV links in Figure 9, it can be derived that the HC group copes with greater cognitive flexibility (less beta band synchronization) through dorsoventral connectivity between the superior marginal gyrus of the ventral attention network and the superior parietal gyrus of the dorsal attention network. Nevertheless, other links may exist connecting parts of the dorsal attention network (e.g., bilateral superior parietal gyrus) to the IFG and middle frontal gyrus, indicating involvement of the SN.

[0099] In the sixth example, links in the HC group with significantly different (i.e., increased) PLV values ​​in the theta band (4-8 Hz) during the Task 2 Go condition are compared with those in the ADHD group. Increased PLV values ​​can be observed between regions of the dorsal attention network (e.g., bilateral superior parietal lobules) and regions of the ventral attention network (e.g., left supramarginal gyrus, right angular gyrus, and inferior parietal gyrus). Two links are shown between the inferior parietal gyrus and bilateral middle frontal gyrus of the ventral attention network. Figure 10 shows links in the central executive network where the HC group has significantly different PLV values ​​compared to the ADHD group during the theta band (4-8 Hz) during the Task 2 Go condition. On the left, diagrams of the posterior, superior, left, and right sides of the brain are shown. On the right, a pie plot shows a schematic of significant links. The lines indicate increased PLV values ​​in the HC group compared to the ADHD group during the Task 2 Go condition.

[0100] In the seventh example, we consider the PLV values ​​in the No-Go conditions for both Task 1 and Task 2. Figure 11 shows links in the alpha band that have significantly different (i.e., increased) PLV values ​​between the HC group and the ADHD group in the No-Go condition (consonant or red vowel). As in the previous case, the illustrated network is the SN. Increased PLV values ​​in HC compared to the ADHD group can be observed in the links between nodes in the SN (e.g., left insula, bilateral infraorbital frontal gyrus, bilateral anterior cingulate cortex). In this No-Go condition, negative information bits stored in the TPJ can be transmitted to the dorsal attention network through the SN. Therefore, the increase in alpha band PLV values ​​in the HC group can mean better suppression of irrelevant stimuli (e.g., good management of negative information). This can be observed as a higher accuracy rate in the no-go condition for the ADHD group (6.04% for ADHD vs. 4.04% for HC, p-value = 0.031). Figure 11 shows the links in the spleness network where the HC group had significantly different PLV values ​​compared to the ADHD group in the No-Go condition of Task 1 (consonant or red vowel) in the alpha band (8-12 Hz). The left side shows diagrams of the posterior, superior, left, and right sides of the brain. On the right side, the pie plot shows a schematic diagram of significant links. The lines indicate that the PLV values ​​increased in the HC group compared to the ADHD group in the No-Go condition of Task 1 (consonant or red vowel).

[0101] Furthermore, Figure 12 shows links in the beta band (20-30 Hz) that have significantly reduced PLV values ​​in the HC group compared to the ADHD group in the No-Go condition of Task 1 (consonants and red vowels). In the No-Go condition of Task 1, red vowels can represent top-down violations where a reduction in the beta band may be desirable to provide cognitive flexibility. This can explain the superior performance of the HC group in Task 1 compared to the ADHD group. Again, these links suggest that the HC group addresses greater cognitive flexibility (reduced beta band synchronization) in the interaction between parts of the ventral attention network (right angular gyrus, right supramarginal gyrus) and parts of the dorsal attention network (right superior parietal gyrus) via a direct dorsal-ventral pathway, and further via the SN with the involvement of parts of the IFG and middle frontal gyrus. In particular, Figure 12 shows the links in the central executive network where the HC group had significantly different PLV values ​​compared to the ADHD group in the No-Go condition of Task 1 (consonant or red vowel) in the beta band (20-30 Hz). The left side shows diagrams of the posterior, superior, left, and right sides of the brain. On the right side, the pie plot shows a schematic diagram of significant links. The lines indicate that the PLV values ​​of the HC group were reduced compared to the ADHD group in the No-Go condition of Task 1 (consonant or red vowel).

[0102] As mentioned earlier, Bayes' theorem can be expressed in terms of Shannon information. By applying the -log function to both sides of Bayes' theorem, we can define a Shannon metric called information conveyed (TIC), which measures the information conveyed by a stimulus and can reduce (if the stimulus is informative or TIC > 0) or increase (if the stimulus is non-informative or TIC < 0) uncertainty about behavioral choices. Whether a stimulus is an informative or non-informative feature may depend on its behavioral relevance, for example, in sensorimotor cognitive tasks as described above. TIC may also be a function of Shannon information, and at the same time, it can enable updating of the Bayesian model by measuring the increase or decrease in uncertainty about behavioral uncertainty (for example, harmonizing Shannon surprise and Bayes surprise).

[0103] In the alpha band (8-12 Hz), the PLV values ​​of the HC group in the No-Go condition (consonants or red vowels) of Task 1 were significantly reduced compared to the PLV values ​​in the Go condition (vowels other than red), suggesting that the ventral attention network was deactivated by negative information related to Bayesian likelihood. Furthermore, when these negative top-down bits are subtracted from the bottom-up bits corresponding to the Shannon surprise of the stimulus, negative TICs are generated in the anterior inferior temporal lobe, which can deactivate the visual cortices of the VVP and occipital cortex. Interestingly, one of the areas with significantly different FC values ​​between the No-Go and Go conditions is the left fusiform gyrus, which responds more strongly to letters than to numbers. This is consistent with the nature of Task 1, where the stimulus is a letter.

[0104] Furthermore, in the alpha band (8-12 Hz), the PLV values ​​of the HC group were compared to those of the HC group performing Task 1 under the No-Go condition when the stimulus was the red vowel e, compared to Task 2 under the Go episode condition (red vowel e). In the No-Go condition of Task 1 (red vowel e), the color bias network was deactivated compared to the same stimulus in Task 2 where uncertainty about behavior a was completely canceled out by the episode TIC, and therefore, it was observed that the aforementioned color bias network could be strengthened. Not only the color bias network, but the entire dorsal-ventral attention network described above was activated by the HC group, and it was observed that they could manage the top-down violation that the red vowel e signifies in Task 2.

[0105] The results above may indicate inefficiency in ADHD subjects because they need to increase PFC activity when dealing with task switching (such as condition exceptions) that involves changes in temporal context or episode. In contrast, the HC group can perform task switching by increasing activity in a new temporoparietal dorsal-ventral attention network without increasing activity in the PFC pathway. In the alpha band, a decrease in PLV values ​​in the ADHD group compared to the HC group in both Go and No-Go conditions is observed in a link that can demonstrate interaction between the dorsal and ventral attention networks through the SN. Therefore, the ventral attention network in the HC group can transmit stronger reorientation (Go condition) or filtering (No-Go condition) signals to the dorsal attention network than the ADHD group. Furthermore, in the link again showing the interaction between the dorsal and ventral attentional networks via the SN, we can observe a significant increase in PLV values ​​in the ADHD group compared to the HC group in the beta band (20-30 Hz) for the No-Go condition of Task 1 (red vowel) and the Go condition of Task 2 (red vowel e). These two conditions can represent top-down violations requiring a specific cognitive flexibility (such as low-beta band synchronization).

[0106] A system for generating scores indicating cognitive impairment, a computer-readable storage medium, and a method for doing so.

[0107] Considering the above considerations and experiments, it is known that we can provide a system, a computer-readable storage medium, and a method that may be suitable for generating a score indicating that a subject (such as a person) has a cognitive disorder, such as ADHD. In particular, the results described above indicate that when a subject has a cognitive disorder (such as ADHD), information in the brain may be transmitted via different pathways (or networks) compared to a healthy subject. A pathway may include two locations (or nodes) in the brain through which information is transmitted. Therefore, in order to generate a score indicating a subject with such a cognitive disorder, it is proposed to look for one or more pathways that distinguish a healthy subject from a subject with a cognitive disorder (such as ADHD) (e.g., a pathway that is activated in a healthy subject and deactivated or less activated in a subject with a cognitive disorder, or vice versa). According to one embodiment, the information transmitted via a particular pathway may correspond to a correlation value (such as the PLV described above) between two time series (time series obtained from an EEG or MEG device) measured at two separate source locations located within the subject's cranial cavity. These two source locations could be two locations in the pathway that distinguishes healthy subjects from subjects with the aforementioned disorders. Furthermore, as previously mentioned, subjects with cognitive disorders such as ADHD may lack the ability to increase activity in the dorsal-ventral attentional network (DVAN) without increasing activity in the PFC pathway (for example, as shown in Figures 1, 2, 5, and 6, healthy subjects can have this ability through the aforementioned dorsal-ventral attentional network (DVAN)).

[0108] A lack of ability to increase activity in the dorsal-ventral network (DVAN) is not limited to subjects with ADHD, but can also be present in subjects with other cognitive disorders (e.g., disorders that may be associated with patients who have difficulty maintaining focus or switching between mental tasks). Therefore, the scores generated by this disclosure may also indicate such cognitive disorders and can assist physicians in diagnosing subjects with such disorders. Furthermore, the scores generated by this disclosure may be used to assess whether a subject is capable of performing certain occupations where maintaining focus or switching between mental tasks is important (e.g., pilot, truck driver, air traffic controller).

[0109] According to the first embodiment, the correlation between pairs of time series of source locations within the subject's cranial cavity (i.e., the subject's brain) included in the PFC pathway can be used to generate a score indicating that the subject has a cognitive disorder such as ADHD. As previously mentioned, subjects with such cognitive disorders may have increased activity in the PFC pathway (e.g., compared to subjects without cognitive disorders). According to one embodiment, the PFC pathway may include the frontal lobe of the brain (e.g., the first source location of the pathway) and the superior parietal lobule (e.g., the second source location of the pathway). Thus, a correlation (corresponding to information transmission) can be calculated based on a pair of time series including a first time series measured in the subject's frontal lobe and a second time series measured in the subject's superior parietal lobule (SPL). In one embodiment, the frontal lobe (i.e., the first source location) may include at least one of the middle frontal gyrus (MFG) and the inferior frontal gyrus (IFG).

[0110] In a second embodiment, a score indicating that a subject has a cognitive disorder, such as ADHD, can be generated using a correlation between a time series of source locations (in the subject's cranial cavity) that are part of the ventral attention network and another time series of source locations (in the subject's cranial cavity) that are part of the dorsal attention network. As illustrated and described above, healthy subjects may have the ability to (directly) transmit information between these two source locations (i.e., healthy subjects can use the dorsal-ventral network (DVAN) described above), but subjects with cognitive disorders, such as ADHD, may lack this ability. Therefore, one or more correlations between pairs of time series between these two source locations may be appropriate for generating a score indicating a cognitive disorder (such as ADHD). In one embodiment, the location that is part of the ventral attention network may be the temporoparietal junction (TPJ), and the location that is part of the dorsal attention network may be the superior parietal lobule (SPL). As mentioned above, the TPJ may include the posterior part of the superior temporal sulcus (STS), the posterior part of the superior temporal gyrus (STG), and the inferior parietal lobule (IPL). The inferior parietal lobule includes the angular gyrus (AG) and the supramarginal gyrus (SMG).

[0111] In a third embodiment, a correlation between a time series of a source location (inside the subject's cranial cavity) that is part of the ventral attention network and a location that is part of the ventral visual pathway (VVP) can be used to generate a score indicating that a subject has a cognitive disorder, such as ADHD. As illustrated and described above, healthy subjects can transmit information between these two locations, but subjects with cognitive disorders, such as ADHD, may lack this ability. Therefore, the correlation between pairs of time series of these two locations may be appropriate for generating a score indicating a cognitive disorder (such as ADHD). In one embodiment, the location that is part of the ventral attention network may be the TPJ, and the location that is part of the ventral visual pathway may be the inferior temporal gyrus.

[0112] It is clear that not only a single correlation value can be used to generate a score. Instead, multiple correlation values ​​(such as multiple correlation values ​​from the first, second, or third embodiment described above) can be used. Furthermore, by combining the first through third embodiments described above, one or more correlation values ​​obtained from one of these embodiments can be used together with one or more correlation values ​​obtained from another one or both of these embodiments to generate a score indicating that a subject has a cognitive disorder such as ADHD.

[0113] Figure 13 shows an exemplary system 100 that can be used to generate a score 150 indicating that a subject 110 has a cognitive disorder such as ADHD. The system 100 may comprise an EEG or MEG device 120 for measuring electroencephalography (EEG) or magnetoencephalography (MEG) data 130 and a data analysis device 140 for generating the score 150. In one embodiment, the system 100 may further comprise a task presentation device 160 for presenting one or more tasks to the subject 110 and an input device 170 for receiving input from the subject 110 as the subject's response to the presented tasks. In a further embodiment, the system 100 may further comprise a score presentation device 180 for presenting the score 150. In one embodiment, the score 150 is presented to a physician 190, who can diagnose whether the subject 110 has a cognitive disorder such as ADHD.

[0114] The EEG / MEG device 120 may be a standard EEG or MEG device known in the prior art. The EEG / MEG device 120 can measure multiple signals from various source locations located within the cranial cavity of the subject 110. Each source location may correspond to a known brain region such as a lobe, lobule, sulcus, or gyrus. In some examples, multiple signals can be measured while the subject 110 performs one or more tasks that may be presented to the subject 110 by a task presentation device 160. The signals measured by the EEG / MEG device 120 can be divided into one or more bands based on the frequency bands of the signals. For example, the signals may include (in particular) an alpha band (e.g., a frequency band of 8–12 Hz), a beta band (e.g., a frequency band of 12–30 Hz), and / or a theta band (e.g., a frequency band of 4–8 Hz). In one or more embodiments, the signals may correspond to measurements from the electrodes of the EEG device or measurements from the sensor coil of the MEG device, respectively. In some embodiments, the signal may correspond to an aggregated signal (such as an average signal) across measurements from multiple electrodes (in the case of EEG) or multiple sensor coils (in the case of MEG). Such an aggregated signal may correspond to a brain region that includes the location of each of the signals aggregated in the aggregated signal.

[0115] The data analysis device 140 is configured to receive EEG / MEG data 130 and generate a score 150 indicating that the subject 110 has a cognitive disorder such as ADHD. The data analysis device 140 can be located in the same location as the EEG / MEG device 120 (e.g., on-premises) (for example, the data analysis device may be a computing system located in the hospital where the EEG / MEG device 120 is located), or the data analysis device 140 can be located elsewhere (e.g., off-premises such as in the cloud) and communicate with the EEG / MEG device 140, input device 170, and / or score display device 180 via a network (such as the Internet). Further details about the data analysis device 140 will be described below with reference to Figure 2.

[0116] The task presentation device 160 can present one or more tasks to subject 110 while the EEG / MEG device 120 is measuring the subject 110's brain signals. In one embodiment, the task presentation device 160 can present a first task during a first period in which the MEG / EEG device 120 is measuring the subject 110's brain signals. The first task may include three conditions: a first condition, a second condition, and an exception to the second condition (exception condition). Each condition may include one or more stimuli s and a corresponding action a performed by subject 110 when one of the stimuli is presented to subject 110. In one example, subject 110 may be instructed not to interact with the input device 170 (e.g., not to press a button) when the first condition is presented during the first task, to interact with the input device 170 (e.g., to press a button) when the second condition is presented during the first task, and not to interact with the input device 170 (e.g., to press a button) when an exception to the second condition is presented. In one embodiment, the first task may be an example of Task 1, which is described further (in the context of Figure 3) and described in more detail in the context of Figures 18A, 18B and 20-1 to 20-4.

[0117] Furthermore, the task presentation device 160 can present a second task during a second period (the second period is after the first period) while the MEG / EEG device 120 is (still or again) measuring the brain signals of the subject 110. In one embodiment, the second task may include three conditions, as in the first task described above, and an additional fourth condition, which is an exception to the exceptions of the second conditions, so that the subject 110 may be instructed to interact with the input device when the fourth condition occurs during the second task. In one example, the second task may include a first condition in which the subject 110 is instructed not to interact with the input device 170, and a second condition in which the subject 110 is instructed to interact with the input device 170. Furthermore, the second task may include a third condition defining an exception to the second condition, for which the subject 110 is instructed not to interact with the input device 170. Furthermore, the second task may include a fourth condition defining an exception to the second condition, for which the subject 110 is instructed to interact with the input device 170. In one embodiment, the second task may be an example of the task 2 described above. The task presentation device 160 may be any device suitable for presenting one or more tasks, such as a screen (e.g., a tablet or computing device screen).

[0118] The different conditions of the first and second tasks may occur multiple times during the first and second periods, respectively, for example, periodically (e.g., every 5 seconds). Furthermore, the different conditions may occur at a predefined frequency for each of the different conditions (for example, each condition may occur at the same frequency, 1 / 3 of the time in the first task and 1 / 4 of the time in the second task). The brain's response to each different condition can be identified by matching the brain signals measured by the MEG / EEG device 120 with the multiple times the different conditions are presented by the task presentation device 160. Each of the different conditions (i.e., the three conditions of the first task and the four conditions of the second task) may correspond to a different stimulus to the brain of subject 110.

[0119] The input device 170 may be any device capable of receiving input from the subject 110 through interaction between the subject 110 and the input device 170. In one embodiment, the input device 170 may include a button that the subject 110 can press. In another embodiment, the input device may detect the subject 110's gestures or facial expressions that correspond to the interaction between the subject 110 and the input device 170. Using the input from the subject 110 received by the input device 170, it is possible to determine whether the interaction between the subject 110 and the input device 170 was a correct (or incorrect) action in response to the presented conditional stimulus for the task presented by the task presentation device 160. As described above, the subject 110 may be instructed to interact with the input device 170 under some conditions (such as the second condition and exceptions to the second condition) and not to interact with the input device under other conditions (such as the first condition and exceptions to the second condition). Using the input from subject 110, it is possible to determine whether subject 110 interacted with the input device 170 according to these instructions (correct response) or not (incorrect response).

[0120] The score presentation device 180 may be any device capable of presenting one or more scores 150 to the physician 190, allowing the physician 190 to diagnose whether the subject 110 has a cognitive disorder such as ADHD. In particular, the one or more scores 150 presented by the score presentation device 180 may be helpful in the physician 190's diagnosis of whether the subject 110 has a cognitive disorder such as ADHD.

[0121] The EEG / MEG data 130 can be transmitted to a data analysis device 140. In one embodiment, the EEG / MEG data 130 may include brain signals measured by an EEG / MEG device 120. In one embodiment, the EEG / MEG data 130 may include first EEG / MEG data that can be measured while subject 110 is performing one task (such as the first task described above) and second EEG / MEG data that can be measured while subject 110 is performing another task (such as the second task described above). In one embodiment, the brain signals correspond to raw brain signals (such as brain signals measured by an EEG / MEG device 120 without preprocessing). In another embodiment, the brain signals may be preprocessed (for example, by removing external noise or artifacts from the eyes, heart, and / or muscles) before transmitting the EEG / MEG data 130 to the data analysis device 140. In one embodiment, the EEG / MEG data 130 may include all brain signals measured by an EEG / MEG device 120. In another embodiment, the EEG / MEG data 130 may include only a subset of brain signals measured by the EEG / MEG device 120. The subset of brain signals may be brain signals from intracranial locations of the subject 110 as described in the first, second, and third embodiments, for example, from the frontal lobe (e.g., IFG and / or MFG) and superior parietal lobe (see the first embodiment described above), from locations that are part of the ventral attention network (e.g., TPJ) and locations that are part of the dorsal attention network (e.g., superior parietal lobe, see the second embodiment described above), and / or from locations that are part of the ventral attention network (e.g., TPJ) and locations that are part of the ventral visual pathway (e.g., inferior temporal gyrus, see the third embodiment described above).

[0122] In one embodiment, the EEG / MEG data 130 may further include information (such as time information) that allows the time at which the task conditions were presented to the subject 110 by the task presentation device 160 to be matched with brain signals measured by the EEG / MEG device 120. Furthermore, the EEG / MEG data 130 may include data from the input device 170 that indicates the interaction between the subject 110 and the input device 170.

[0123] Figure 2 shows an example of data analysis devices 140 and 200. As mentioned above, data analysis devices 140 and 200 may be on-premises (e.g., located within the local area network of the EEG / MEG device 120) or off-premises (e.g., located in the cloud). For example, the data analysis device may be a computing system (e.g., a server). The data analysis devices 140 and 200 may include a receiving component 210, a time series determination component 220, a correlation value calculation component 230, a score generation component 240, and a score output component 250.

[0124] The receiving component 210 may be configured to receive EEG / MEG data 130, such as the first EEG / MEG data and / or the second EEG / MEG data described above. In one embodiment, the receiving component 210 can receive EEG / MEG data 130 from the EEG / MEG device 120.

[0125] The time series determination component 220 may be configured to determine a first set of time series based on the received EEG / MEG data 130 (for example, based on first EEG / MEG data measured while subject 110 is performing a task). Each of the first set of time series may correspond to a respective source location within the subject's cranial cavity. Furthermore, the time series determination component 220 may be configured to determine a second set of time series based on the received EEG / MEG data 130 (for example, based on second EEG / MEG data measured while subject 110 is performing another task). Each of the second set of time series may correspond to a respective source location within the subject's cranial cavity. For example, the first set of time series and the second set of time series may correspond to the same respective source locations.

[0126] In one embodiment, determining a first (and / or second) set of time series may include filtering the first (and / or second) set of EEG / MEG data 130 so that the first (and / or second) set of time series includes only a subset of all available time series (such as a subset of all available time series that can be obtained from the EEG / MEG device 120). The subset may include only time series between the source locations (such as the frontal lobe, SPL, TPJ, and / or ITG) described above with respect to the first, second, and third embodiments. Furthermore, filtering may also include filtering certain frequencies of the EEG / MEG data (such as restricting the time series to the alpha, beta, and theta bands, restricting it to the alpha band only, restricting it to the beta band only, and / or restricting it to the theta band only). Filtering the EEG / MEG data 130 can reduce the amount of data that needs to be processed by the data analysis device 140. For example, filtering the EEG / MEG data 130 can help obtain the data that needs to be processed for analysis by the data analysis device 140.

[0127] Furthermore, according to one embodiment, determining a first (and / or second) set of time series may include preprocessing the first (and / or second) EEG / MEG data 130 by removing external noise from the EEG / MEG data (e.g., brain signals measured by the EEG / MEG device 120), removing eye, heart, and muscle artifacts, and segmenting the EEG / MEG data 130 so that the time series have the same length (e.g., 1 second). In one embodiment, the time series determination component 220 may be configured to match the time when the task conditions were presented to the subject 110 by the task presentation device 160 with the EEG / MEG data 130 (e.g., brain signals measured by the EEG / MEG device 120), so that each of the determined time series includes the start time of each of the task conditions. This ensures that the subject's brain response to the conditions is included in the time series. In a particular embodiment, the preprocessing may be performed according to the examples of preprocessing further described above with respect to experimental results.

[0128] It is clear that the time series determination component 220 can determine not just one time series but multiple time series. In particular, as mentioned above, the time series determination component 220 can determine each time series for each source location, for each condition of a particular task, and for each subject being examined. The amount of time series to be determined and / or processed can be reduced by considering only a subset of all available time series (as mentioned above).

[0129] The correlation value calculation component 230 can calculate correlation values ​​based on pairs of time series included in a plurality of time series (e.g., a first plurality of time series or a second plurality of time series) determined by the time series determination component 220. In particular, in one embodiment, the correlation value calculation component 230 can calculate a first correlation value for a first pair of time series, the first pair of time series included in the determined first plurality of time series. Furthermore, the correlation value calculation component 230 can calculate a second correlation value for a second pair of time series, the second pair of time series included in the determined first plurality of time series. Furthermore, the correlation value calculation component 230 can calculate a third correlation value for a third pair of time series, the third pair of time series included in the determined first plurality of time series. Furthermore, the correlation value calculation component 230 can calculate a fourth correlation value for a fourth pair of time series, the fourth pair of time series included in the determined second plurality of time series. In one embodiment, the correlation value can be associated with the phase of the time series pair (for example, the correlation value can be given by the PLV described above or by the circular correlation coefficient). In another embodiment, the correlation value can be associated with the amplitude of the time series pair (for example, the correlation value can be given by the Pearson correlation).

[0130] The score generation component 240 can generate one or more scores 150 based on a first correlation value, where one or more scores indicate that the subject 110 has a cognitive disorder such as attention deficit hyperactivity disorder (ADHD). In one embodiment, each of the one or more scores 150 can be given by a corresponding correlation value calculated by the correlation value calculation component 230. In one embodiment, the score may be a correlation value for a time series corresponding to the frontal lobe and a time series corresponding to the SPL (a first pair of time series). Another score may be a correlation value for a time series corresponding to the SPL and a time series corresponding to the TPJ (a second pair of time series). Yet another score may be a correlation value for a time series corresponding to the TPJ and a time series corresponding to the ITG (a third pair of time series). In one embodiment, these scores can be combined into a single score, and in another embodiment, all three scores can be output.

[0131] Based on the aforementioned considerations (TIC, its top-down contribution, and / or the dorsal-ventral attentional network (DVAN), etc.) and experimental results, a high correlation between time series corresponding to the frontal lobe and time series corresponding to the SPL can indicate that the subject has a cognitive disorder such as ADHD (because the PFC pathway may be increased due to a lack of dorsal-ventral transmission). Similarly, a high correlation between time series corresponding to the TPJ and time series corresponding to the SPL can indicate that the subject does not have a cognitive disorder (because dorsal-ventral transmission may be activated). Furthermore, a high correlation between time series corresponding to the TPJ and time series corresponding to the ITG can indicate that the subject does not have a cognitive disorder (because dorsal-ventral transmission may be activated). A physician presented with one or more scores (such as one or more of the three scores mentioned above) can use those scores to diagnose whether the subject has a cognitive disorder such as ADHD.

[0132] In addition, or instead, one or more scores 150 can be generated based on comparing the correlation value calculated by the correlation calculation component 230 with one or more other values ​​(e.g., values ​​obtained from second EEG / MEG data). In particular, a comparison value can be calculated between the correlation value and the other values, and a score can be generated based on the comparison value.

[0133] In one embodiment, a correlation value can be compared to another correlation value, which is obtained for a different time series pair from the same source location, the same subject, and the same task, but for which a different condition of the task may be presented to the subject 110 (e.g., via the task presentation device 160). For example, the condition for the correlation value may be the second condition of the first task described above, and the condition for the other correlation value may be an exception to the second condition of the first task described above. In another example, the condition for the correlation value may be an exception to the second condition of the second task described above, and the condition for the other correlation value may be an exception to the exception of the second task described above. The score generation component 240 may be configured to calculate a comparison value between these two correlation values. In one example, the score 150 may be the comparison value. In this case, the score can indicate the subject's ability to deal with the exception. Another correlation value can be calculated by the correlation value calculation component 230 in the same manner as calculating the correlation value (e.g., based on a time series from second EEG / MEG data).

[0134] In another embodiment, a correlation value can be compared to another correlation value, which is obtained for a different pair of time series from the same source location, the same subject, and the same conditions, but while subject 110 is performing a different task (e.g., presented via task presentation device 160). For example, the task for the correlation value may be the first task described above (with no exceptions to the second condition), and the other task may be the second task described above (with exceptions to the second condition). As described above, the subject may perform the second task after the first task, and therefore the subject needs to switch from a state of not interacting with the input device 170 in the first task to a state of interacting with the input device 170 in the second task (whereas the task presentation device 160 may present the same stimulus). The score generation component 240 may be configured to calculate a comparison value between these two correlation values. In one example, the score 150 may be the comparison value. In this case, the score can indicate the subject's ability to cope with switching from one task to another (i.e., reorientation). Another correlation value can be calculated by the correlation calculation component 230 in the same way that the correlation value is calculated (for example, based on a time series from a second EEG / MEG data).

[0135] In yet another embodiment, a correlation value can be compared to another correlation value, which was obtained for a different pair of time series from the same source location, the same conditions, and the same task, but for at least one other subject who is known to have a cognitive disorder such as ADHD. This other correlation value can be stored in the data store of data analyzers 140, 200 (not shown in Figure 2). Similarly, a correlation value can be compared to another correlation value, which was obtained for a different pair of time series from the same source location, the same conditions, and the same task, but for at least one other subject who does not have a cognitive disorder such as ADHD. Again, this other correlation value can be stored in the data store of data analyzers 140, 200.

[0136] Furthermore, a score can be generated based on information from the input device 170 indicating whether the subject 110 interacted with the input device accurately according to the task conditions. In one example, a large number of incorrect responses may indicate that the subject has a cognitive disorder.

[0137] Each of the aforementioned scores can be output separately as one or more scores 150 to assist, for example, a physician 190 in diagnosing a subject 110. Alternatively, a combined score can be generated (for example, based on a weighted average of the aforementioned scores), and this combined score can be output to the physician 190.

[0138] The score output component 250 is configured to output one or more scores 150. In one example, the score output component 250 can send one or more scores 150 to a score presentation device 180, which can then present one or more scores to a physician 190.

[0139] Figure 15 shows the brain within the cranial cavity 300 of subject 110. The brain may include the frontal lobe 310, parietal lobe 370, occipital lobe 380, and temporal lobe 390. As previously mentioned, subjects with cognitive disorders such as ADHD may lack the ability to activate the dorsal-ventral network (DVAN), which can link the ventral attention network region with the dorsal attention network region, and / or the ventral attention network region with the ventral visual pathway region. Instead, subjects with such cognitive disorders may show increased activity in the PFC pathway. Accordingly, according to the embodiment, one or more scores indicating that a subject has such cognitive disorder can be generated based on at least one of the following: a correlation between a time series from the subject's frontal lobe 310 (e.g., from MFG320 and / or IFG330) and a time series from SPL340; a correlation between the subject's time series from SPL340 and a time series from TPJ350; and a correlation between the subject's time series from TPJ350 and a time series from ITG360. Furthermore, a correlation for a pair of time series can be used for any of these three correlations (which can also be combined as further described above), where the source location of the first time series (of the pair of time series) is the starting location of the link (i.e., the link shown in any one of Figures 4 to 12), and the source location of the second time series (of the pair of time series) is the ending location of the same link. An example is the correlation between time series from the left superior marginal gyrus (ISMG) and the left inferior temporal cortex (IITG), shown as a link in Figure 4. Another example is the correlation between time series from the left superior parietal gyrus (ISPG) and the left inferior frontal gyrus orbitofrontal gyrus (IIFGo), shown as links in Figure 6. Using other links shown in Figures 4-12, it is clear that the correlation values ​​used to generate scores indicating that a subject has cognitive impairment can be calculated, as previously mentioned.

[0140] Figure 16 shows a computer implementation method 400 according to one embodiment of the present disclosure. The steps of the method shown in Figure 16 and described below can also be implemented on a computer-readable storage medium on which a computing system (such as a data analysis device 140) performs these steps. In the first step 410, the subject's EEG / MEG data 130 (such as first EEG / MEG data or second EEG / MEG data) is received. In the next step 420, multiple time series are determined based on the EEG / MEG data. For example, the first multiple time series are determined based on the first EEG / MEG data, and the second multiple time series are determined based on the second EEG / MEG data. Each time series in the multiple time series may correspond to a respective source location within the cranial cavity 300 of the subject 110. Next, in step 430, correlation values ​​can be calculated for pairs of time series included in the multiple time series. In step 440, a score 150 is generated based on the correlation values. A score of 150 may indicate that the subject has a cognitive disorder, such as attention deficit hyperactivity disorder (ADHD). Next, in step 450, the generated score is output.

[0141] Further examples that may help in understanding the present invention Systems and methods consistent with this disclosure are directed toward the efficient and accurate detection of ADHD in a person. The systems and methods described below include techniques that present a simple task for accurately detecting the presence of ADHD and the probability of certainty of ADHD. The present invention further identifies metrics for modeling the task and calculating the probability of certainty of ADHD. In some embodiments, the disclosed techniques include a plurality of tasks for presenting content under different conditions that evoke a response to the content. As described below, special tasks with response conditions can result in various technical improvements to the accuracy of ADHD detection using the underlying systems, hardware, and software, as well as other applications running on the underlying hardware and software.

[0142] Figure 17 is a block diagram showing various exemplary components of an attention deficit detection system (ADDS) 1700 for accurately detecting ADHD, according to some embodiments of the present disclosure. In one embodiment, ADDS 1700 may be the system 100 shown in Figure 13 and described above. ADHD detection may include evaluating the user of ADDS 1700 to detect the presence of ADHD and the degree of certainty of ADHD. The measure of the ADHD index may be based on the amount of agreement between the user's response and the expected outcome of the task. In some embodiments, ADDS 1700 may measure the amount of task response deviation from a task response defined by the user of ADDS 1700 as part of the initialization of ADDS 1700. The user of ADDS 1700 may define a scale of attention levels of a healthy person when setting up ADDS 1700. For example, user 1760 may set up ADDS 1700 by providing a text configuration file that is parsed by ADDS 1700.

[0143] As shown in Figure 17, the ADDS 1700 may include a measurement module 1710 that evaluates a person's attention level and a database 1730 that stores the evaluated attention level in a performance metric 1734. The measurement module 1710 can help determine the attention level using data from the database 1730. The database 1730 can help evaluate the attention level based on a control group 1731 associated with the user 1732 through a task definition 1733. The reporting module 1720 can evaluate the attention level and help determine the attention deficit of the user 1732 associated with the control group 1731. The ADDS 1700 can determine the attention levels of the control group 1731 and the user 1732 using responses 1740 provided using a user device 150. The user device 1750 may be a processor or a complete computing device such as a laptop, desktop computer, mobile device, smart home appliance, or IoT device.

[0144] The measurement module 1710 can measure the attention level of a user (e.g., user 1760) by measuring vibrational signals in different areas of the user's brain. The measurement module 1710 can measure the attention level of user 1760 by determining the level of connectivity between different areas of user 1760's brain. The measurement module 1710 can measure vibrational signals when it receives user 1760's response 1740 from user device 1750. The ADDS 1700 may be configured to allow the measurement module 1710 to collect signals from different brain regions based on the response. The configuration may include whether to collect brain signals, when to collect brain signals, and from which regions to collect brain signals. A user of the ADDS 1700 can configure the measurement module 1710 using the configuration provided via user device 1750 on network 1770.

[0145] The reporting module 1720 can evaluate the measured signal to determine the presence of ADHD and calculate the degree of certainty of ADHD. The reporting module 1720 can evaluate the signal by measuring the signal connectivity. In some embodiments, the reporting module 1720 can evaluate the signal by comparing the determined connectivity levels. Detailed descriptions of various evaluation techniques are shown in the descriptions of Figures 19 and 20-1 to 20-4 below.

[0146] In various embodiments, the database 1730 can take several different forms. For example, the database 1730 may be an SQL database or a NoSQL database, such as a database developed by Microsoft®, REDIS, Oracle®, Cassandra, or MySQL, or it may be a database of various types that include data returned by calls to web services, data returned by calls to computational functions, sensor data, IoT devices, or various other data sources. The database 1730 can store data used or generated during the operation of the application, such as data generated by the measurement module 1710. For example, if the measurement module 1710 is configured to evaluate a response 1740, the measurement module 1710 may access the task definition 1733 to share the stimulus 1780, measure the signal using the connectivity level when it receives the response 1740, and store it as a performance metric 1734. Similarly, if the reporting module 1720 is configured to determine ADHD, the reporting module 1720 can access the performance metric 1734 and obtain the evaluated signal connectivity level associated with a user (e.g., user 1760) for a stimulus, by comparing it with the signal connectivity level for the same stimulus provided to the control group 1731. In some embodiments, the database 1730 can be supplied with data from external sources (e.g., servers, databases, sensors, IoT devices, etc.). In some embodiments, the database 1730 can provide data storage for a distributed data processing system (e.g., Hadoop distributed file system, Google File System, ClusterFS, and / or OneFS).

[0147] Network 1770 can take various forms. For example, Network 1770 may include, or utilize, the Internet, a wired wide area network (WAN), a wired local area network (LAN), a wireless WAN (e.g., WiMAX), a wireless LAN (e.g., IEEE 802.11), a mesh network, a mobile / cellular network, an enterprise or private data network, a storage area network, a virtual private network using a public network, or other types of network communications. In some embodiments, Network 1770 may include an on-premises (e.g., LAN) network, and in other embodiments, Network 1770 may include a virtualization (e.g., AWS®, Azure®, IBM Cloud®, etc.) network. Furthermore, in some embodiments, Network 1770 may be a hybrid on-premises and virtualized network that includes components of both on-premises and virtualized network architectures.

[0148] ADDS1700 can provide a stimulus 1780 to a user 1760 of user device 1750 by presenting content that matches the conditions of task definition 1733. User 1760 provides a response 1740 to the stimulus 1780 using a device attached to user device 1750. For example, user 1760 of user device 1750 can respond to a task presented to user 1760 by clicking a button or touching the screen in response to the stimulus 1780. A response 1740 to a stimulus associated with a task may include sharing a response to the task. For example, a task may include a question that provides a list of response options that user 1760 can select in response to the question. In some embodiments, the response 1740 may include the user's no response to the task. No response can be recorded based on the absence of a response to a stimulus after a certain period of time. The period for determining no response to a stimulus may be configurable by the user of ADDS1700 (e.g., user 1760).

[0149] Task definition 1733 includes a set of response rules that may be presented in advance to the user 1760 of the user device 150 in order to respond to a stimulus 1780 based on the task rules of task definition 1733. The rules may include when and how to respond. In some embodiments, the rules may include a set of conditional statements that subdivide response types and response times based on content that satisfies the task definition. In some embodiments, a task may include rules that extend the rules of another task.

[0150] Figures 18A and 18B are tables of exemplary tasks for detecting ADHD and its probability, according to certain embodiments of the present disclosure. The tables include various response conditions, indicated as row and column headers 1810-1840 and 1860-1890. As shown in Figure 18A, row headers 1810 and 1820 divide the possible content presented to the user (e.g., user 1760 in Figure 17) on the user device (e.g., user device 1750 in Figure 17). The divided content indicates when the user is expected to respond to the content presented as stimulus 1780 on the user device 1750. For example, task 1800 divides the content into consonants and vowels, with the rule that the user does not respond when a consonant is presented as a stimulus. Tasks may include additional conditions to restrict responses to stimuli presented to the user (e.g., stimulus 1780 in Figure 17). Task 1800 includes additional conditions as column headers (e.g., column headers 1830, 1840). Once the first condition for a response is met, additional conditions can be added to restrict it. For example, as shown in Figure 18A, Task 1800 includes a second condition for the color of the letters, restricting the response to vowels when the vowel is red.

[0151] A task can include multiple subconditions that add different restrictions or permissions. For example, Figure 18B shows task 1850 in tabular form, which may include one condition that determines whether the vowel is the letter "E" or another vowel, and a second condition that determines whether the letter color is "red" or "another color".

[0152] A response is an action taken by a user (e.g., user 1760) on a user device (e.g., user device 1750). Responses can include positive responses (e.g., clicking a button) and negative responses (e.g., clicking another button). In some embodiments, a negative response may be no response at all. For example, in task 1800, when a consonant is displayed (rule 1810), user 1760 is expected to have a negative response by not responding to the displayed character. A task condition can divide possible areas of content into active and silent states. The active and silent states of a condition indicate when user 1760 is expected to respond to or not respond to instances from the area of ​​content presented as stimulus 1780. For example, as shown in Figure 18A, task 1800 includes an area of ​​characters as content, divided into consonants associated with negative responses and vowels associated with positive responses. In some embodiments, a task may include restrictions on the condition to further limit the expectations of the active state. For example, in task 1800, condition 1820 may have a restriction that does not respond when the displayed vowel is displayed in red. In some embodiments, a second condition may restrict content that belongs to both the active and silent states.

[0153] Figure 18B shows a table for Task 1850, which has additional conditions for removing restrictions on active state content. As shown in Figure 18B, rules 1860 and 1870 include the active and silent states of the first condition, similar to rules 1810 and 1820 of the first condition in Task 1800. Similar to Task 1800, Task 1850 includes a second condition with rules 1880 and 1890 for imposing restrictions when the first condition matches the active state. A third condition, in addition to the second condition, can split the active state content. The third condition splits the active state content to remove the restrictions imposed by rules 1880 and 1890 of the second condition. For example, as shown in Figure 18B, Task 1850 includes a third condition that splits the active state vowel content into the letter "E" and other vowels. When the third condition is positive, the task can remove the restrictions on responses imposed on the active state content by the second condition. For example, in task 1850, the second condition rule 1880 imposes a restriction that the system does not respond when the vowel is "red," but the third condition rule 1875 removes this restriction for the letter "E" by allowing a response.

[0154] ADDS1700 can present a user (e.g., user 1760) with content belonging to a content area that satisfies a first, second, or third condition defined in tasks 1800, 1850 regarding responses, along with rules for the expected response. ADDS1700 can then collect the response (e.g., response 1740 in Figure 17) and the signals generated in user 1760's brain when response 1740 was presented. The brain signals collected when user 1760 received response 1740 to the display of stimulus 1780 are evaluated to determine the level of connectivity between the brain regions of user 1760 from which the brain signals were collected. A detailed description of the different brain regions and the determination of connectivity levels is provided in the description of Figure 19 below.

[0155] Figure 3 shows the conditional probability of each output based on the cascade model according to some embodiments of the present disclosure. Conditional probability and information transmission are calculated using the transmission information content (TIC) metric shown below. For example, using the following TIC metric, in task 1800, when consonant data is observed by a user (e.g., user 1760 in FIG. 1), the TIC metric calculates the amount of information transmission as follows using the TIC metric defined below.

Number

[0156] In the scenario, I(s) << I(s|a) ⇒ TIC(s→a) < 0, that is, there is negative information transmission from stimulus 1780, including the display of consonants as part of task 1800 that elicits a response. The negative information bit is an indicator that the user 1760 is suppressing or restricting himself from showing a positive response (e.g., clicking a button). As a result, since I(a|s) is much larger than I(a), the button is not pressed.

[0157] When vowel data is presented to user 1760 as part of task 1800, user 1760 needs to check whether the color of the character is red (e.g., a red vowel). For vowels other than red, information transmission is calculated as follows using the conditional TIC metric.

Number

[0158] When red vowel data is presented to user 1760 as part of task 1800, user 1760 needs to check whether the character is red. Applying the conditional TIC to the red context gives the following equation.

Number

[0159] In this case, component I(c|a,s) generates negative information bits that cancel out the information generated by the evidence representing the top-down control performed by the subject. To derive the value of the probability P(c|a,s) of which we are interested in the amount of information, we can infer the probability that the vowel is red, assuming that a vowel appears and the user (e.g., user 1760 in Figure 17) presses a button. The answer is that this probability is very low, and therefore the amount of information associated is very high, i.e., a large amount of negative information bits are generated to cancel out the positive bits generated by the evidence I(c|s).

[0160] Therefore, in this context, the evidence of the red vowel's appearance conveys negative information to action a, which is pressing the button.

[0161] If red vowel data is presented to user 1760 as part of task 1850, user 1760 needs to determine whether the letter is "E". Information transfer is calculated using the episode TIC metric as follows:

number

[0162] The aforementioned amount of information bits can be used as a default value to determine attention levels by comparing different users. The ADDS1700 starts with these default values ​​as expected signal values ​​and can modify them as signals from more people, who are considered to be part of the control group, are added.

[0163] Figure 19 shows a connectivity network between brain regions according to some embodiments of the present disclosure. When a user (e.g., user 1760 in Figure 17) exhibits a response 1740, the ADDS1700 can identify the presence of signals in various brain regions representing different connectivity networks. Using the signals identified in the connectivity network, the ADDS1700 can determine the level of connectivity between different brain regions. The ADDS1700 evaluates the levels of connectivity between various brain regions to determine attention levels and, based on those determined attention levels, determines whether the user has ADHD.

[0164] The ADDS1700 can determine attention levels and deficits by analyzing and using signals of various frequencies. When evaluating signals and their connectivity, the ADDS1700 can group signals by frequency spectrum to determine attention levels and deficits. The ADDS1700 groups signals in the 8–12 Hz frequency band as the alpha group, signals in the 20–30 Hz frequency band as the beta group, and signals in the 4–8 Hz frequency band as the theta group. In some embodiments, the ADDS1700 can collect signals of different frequencies from different areas of the brain at different times.

[0165] Each signal group represents a specific mode of task stimulus provision and response behavior. For example, an alpha-band oscillation amplitude may indicate suppression of activity. In some embodiments, the characteristics of a signal group (e.g., amplitude) can have multiple meanings. For example, the same alpha-band oscillation amplitude may also indicate active processing, which can be considered task-related information. Changes in the characteristics of the signal or signal amplitude can be considered differences in the cognitive activity of the brain of the user 1760 generating the signal.

[0166] In some embodiments, decreased connectivity between brain regions indicates increased attention. Improved connectivity is based on tasks used to activate different regions of the user's brain.

[0167] As shown in Figure 19, disclosed embodiments of ADDS1700 evaluate communication between various known brain regions used to assess attention (e.g., regions within the dorsal attention network (DAN) 1910 or the ventral attention network (VAN) 1920). Disclosed embodiments of ADDS1700 can use the tasks defined in Figures 18A and 18B to generate positive and negative bit information flows occurring between DAN 1910 and VAN 1920 and identify the pathways involved in this information flow. ADDS1700 can also identify alternative pathways and connectivity networks that may contain signals when different people respond to the same stimulus. For example, ADDS1700 can detect signals from the dorsal-ventral attention network (DVAN) 1930.

[0168] ADDS1700 determines whether a user (user 1760 in Figure 17) has ADHD by determining the level of connectivity between brain regions generated in the user's brain and comparing it to the connectivity levels of a known group of healthy individuals. Disclosed embodiments of ADDS1700 provide stimuli using tasks as a way to evoke activity in brain networks, generate signals by the transmission of information (positive and negative bits), and use the connectivity of the generated signals to examine the user's (e.g., user 1760 in Figure 17) ADHD. For example, ADDS1700 can activate and examine the presence of signals in DVAN1930 by using tasks 1800, 1850 and presenting instances of regions of content defined by tasks 1800, 1850 that are presented for the user's response. The use of DVAN1930 is examined by comparing different types of measurement signals evaluated in user 1760 to a previously identified group of healthy users without ADHD (e.g., control group 1731). A detailed explanation of the comparative analysis of signal groups between different users is provided in the descriptions of Figures 20-1 to 20-4 below.

[0169] In some embodiments, attention deficits can also be determined by comparing the variation in connectivity with switched tasks having different rules. In such scenarios, instances of areas of content that have the opposite response defined in the task are selected. For example, the red letter "E" presented as part of task 1800 becomes a no-go condition due to an active state restricted by a negative second condition. The same red letter "E" presented as part of task 1850 becomes a restricted second condition, which is released by a third condition. A detailed explanation of the attention network analysis in the task switching scenario is described in detail in the description of Figure 21 below.

[0170] The disclosed embodiments of ADDS1700 can help model the probability of an ADHD level based on a presented behavioral task. The probability of an ADHD level helps to accurately predict the presence and probability of having ADHD. A model for identifying the probability of an ADHD level can be implemented using the exemplary tasks 1800 and 1850 shown in the description of Figure 3.

[0171] Bayes' brain theory establishes that the entire cortex represents a probability distribution, and this probability distribution changes to an estimate only when a decision is needed. Under Bayes' theorem, the influence of data x as stimulus 1780 on user 1760 to change the prior distribution P(θ) to a posterior distribution P(θ|x) can be expressed as follows: P(θ|x)=P(x|θ)P(θ) / P(x) Here, P(x) is known as the proof of Bayes' theorem, and P(x|θ) is known as the likelihood.

[0172] The same expression, expressed using the -log function, used to represent the amount of information transmitted in the brain when data x is presented as stimulus 1780, is as follows: -log2[P(θ|x)]=-log2[P(x|θ)P(θ) / P(x)] -log2[P(θ|x)]=-log2P(x|θ)-log2P(θ)+log2P(x)

[0173] The information content of event x_j (measured in bits) is also known as self-information and is given by the following formula: I(x i ) = -log2[p(x i )bits

[0174] Therefore, the logarithmic equation expressed as information bits is as follows: I(θ|x)(bits)=I(θ)(bits)-I(x)(bits)+I(x|θ)(bits) The aspect of the above equation is that the aforementioned change in the amount of information of a single model θ in the model space that induces a single observation x when data x is presented does not become more complex, but rather becomes two amounts of information I(x) and I(x|θ) generated by the single observation. Therefore, in the above equation, -I(x)(bits)+I(x|θ)(bits) represents the information transmitted to the single model θ by observation x. The ADDS1700 calculates this information transmission using a new metric called Information Transfer (TIC). TIC is defined as follows: TIC(x→θ)(bits)=I(x)-I(x|θ) I(θ) can be interpreted as the prior uncertainty about the model θ, and I(θ|x) can be interpreted as the posterior uncertainty about the model θ after a single observation x.

[0175] Applying the TIC metric to the above information quantity formula results in the following transformation: I(θ|x)(bits)=I(θ)(bits)-TIC(x→θ)(bits)

[0176] The ADDS1700 uses the TIC metric to describe the information flow. The TIC metric is positive, meaning there is a positive net information transfer, if the negative bits of information in the Bayesian likelihood I(θ|x) do not cancel out the positive bits of information in the evidence I(x). Conversely, the TIC is negative, meaning there is a negative net information transfer, if the negative bits corresponding to I(θ|x) are greater than the bits in the evidence I(x). A detailed explanation of using the gain or loss of these bits in the form of signal transfer between two domains to detect ADHD is shown in detail in the descriptions of Figures 20-1 to 20-4 below.

[0177] Using the aforementioned TIC metric, the amount of information represented using stimuli (e.g., stimulus 1780 in Figure 17) and behaviors (e.g., response 1740 in Figure 17) is transformed as follows: I(a|s)=I(a)-TIC(s→a)(bits) I(a) can be interpreted as the prior uncertainty about action a, and I(a|s) can be interpreted as the posterior uncertainty about action a that the user (for example, user 1760 in Figure 17) takes after the occurrence of stimulus s.

[0178] The amount of information transmitted that elicits or does not elicit action a when stimulus s occurs in the context c defined in tasks 1800 and 1850, using different conditions, is as follows:

number

[0179] ADDS1700 can use a conditional TIC to describe the information bits to be communicated when additional conditions, such as a second condition for task 1800 defined using rules 1830 and 1840, are presented to user 1760.

[0180] In some embodiments, the ADDS1700 can calculate a TIC metric for information transfer that may trigger behavior a, taking into account the occurrence of stimulus s in context c and episode (temporal context) e. Information transfer is as follows: I(a|s,c,e)=-log2[P(a|s,c,e)]

[0181] Similar to the contextual case, ADDS1700 can then apply a conditional version of Bayes' theorem to the posterior probability as follows:

number

number

[0182] Next, sensorimotor TIC, context-conditioned TIC, and episodic-conditioned TIC can be decomposed into two components. I(a|s,c,e)=I(a)-TIC(s→a)-TIC(c→a|s)-[I(e|s,c)-I(e|a,s,c)]=I(a)-I(s)+I(s|a)-I(c|s)+I(c|a,s)-I(e|s,c)+I(e|a,s,c)

[0183] The components I(e|s,c) and I(e|a,s,c) of the episodic conditioned TIC represent the bottom-up and top-down contributions to behavioral choice, respectively. Following the inference of the contextual case, if the episodic conditioned TIC is positive and compensates for the negativity of the contextual conditioned TIC, it is possible that a stimulus that would not provide information in a given context may become informational. ADDS1700 can also interpret I(a) as the prior uncertainty about behavior a, and I(a|s,c,e) as the posterior uncertainty about behavior a after stimulus s occurs in context c and episode e.

[0184] ADDS1700 can use conditional TIC to describe the information bits that are transmitted when additional conditions, such as a second condition of task 1850 defined using rules 1880 and 1890, are presented to user 1760. A detailed description of the conditional probabilities and information bits transmitted when various stimuli are presented to the user (e.g., user 1760 in Figure 1) is shown in the description of Figure 3.

[0185] Figures 20-1 to 20-4 are flowcharts illustrating the operation of an exemplary method for detecting attention deficits according to some embodiments of the present disclosure. For illustrative purposes, the steps of Method 2000 can be performed by ADDS1700. It will be understood that Method 2000 as illustrated can be modified to change the order of the steps and to include additional steps.

[0186] In step 2010, the ADDS1700 can provide a stimulus to the user (e.g., user 1760 in Figure 17) that elicits a response. The ADDS1700 can provide a stimulus by presenting a task (e.g., tasks 1800 and 1850 in Figures 18A and 18B) to the user device (e.g., user device 1750 in Figure 17). The ADDS1700 can present a task and elicit a response by displaying the rules of the task, and then displaying the content to be considered. User 1760 of user device 1750 can respond to the provided stimulus, which is an instance of possible content, based on the rules of the task. The ADDS1700 can provide stimuli sequentially by presenting content that matches the task. In some embodiments, the ADDS1700 can provide stimuli to user 1760 of the ADDS1700 by looping through different tasks.

[0187] In step 2020, the ADDS1700 can acquire signals from sensors about the presented stimulus. The sensors may be electroencephalogram (EEG) or magnetoencephalogram (MEG) devices, or probes for other functional neuroimaging techniques for mapping brain activity, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS). The sensors are positioned on the head to match different known regions (e.g., brain regions 1910-1930 in Figure 19). The ADDS1700 can acquire signals based on a specific trigger event. In some embodiments, the trigger event may include a period of time. The period may include a waiting time since the stimulus was provided in step 2010. The ADDS1700 can enable the configuration of trigger events by allowing selection of trigger events and acquisition of signals and parameters for those trigger events. For example, the ADDS1700 can allow a waiting time after the stimulus is provided before acquiring signals from different brain regions. The EEG / MEG device may be a standard EEG device or MEG device. An EEG / MEG device can measure multiple signals from various source locations within the user 1760's cranial cavity. Each source location may correspond to a known brain region, such as a lobe, lobule, sulcus, or gyrus. In some examples, multiple signals can be measured while the subject performs one or more tasks that may be presented to the user 1760 by the ADDS 1700. In one or more embodiments, the signals may correspond to measurements from electrodes of an EEG device or from a sensor coil of a MEG device. In some embodiments, the signals may correspond to an aggregated signal (such as an average signal) across measurements from multiple electrodes (when using EEG) or multiple sensor coils (when using MEG). Such an aggregated signal may correspond to a brain region containing the location of each signal aggregated in the aggregated signal.

[0188] MEG signals can be measured using a 306-channel (102 magnetometers and 204 planar gradiometers) whole-head MEG Elekta Neuro imaging system. In some embodiments, brain signals can be measured using an online anti-aliasing bandpass filter from 0.1 to 330 Hz and a sampling rate of 1000 Hz. In some embodiments, the head shape of the user (e.g., user 1760 in Figure 17), in addition to three reference points (nasal root and left and right preauricular points), can be acquired using a 3D Fastrak digitizer (Polhemus, Colchester, Vermont). In another embodiment, four head position indicator (HPI) coils can be recorded on the scalp (forehead and mastoid process) of user 1760. In some embodiments, blinks and heart rate can be recorded using two sets of bipolar electrodes, respectively.

[0189] In step 2030, the ADDS1700 can evaluate the signals from each brain region from the brain signals acquired in step 2020. The ADDS1700 can evaluate the signal quality, remove artifacts from the signals, and reconstruct brain activity using signals that approximately represent each brain region, acquired from each sensor on the user's head. The ADDS1700 can improve the data by removing external noise such as eye, heart, and muscle artifacts from the measured EEG / MEG data and by segmenting the EEG / MEG data so that the time series have the same length (e.g., 1 second).

[0190] In step 2031, the ADDS1700 can filter the signal extracted in step 2030 into multiple frequency bands, for example, using a pass filter. The frequency bands may be signal bands related to a function. As illustrated in the description of Figure 19, the signal can be filtered to different frequencies to indicate different attentional behaviors of the ADDS1700 user. For example, the ADDS1700 extracts the signal into theta (4-8 Hz), alpha (8-12 Hz), low beta (12-20 Hz), high beta (20-30 Hz), and low gamma (30-45 Hz) frequency bands. In some embodiments, the frequency bands representing theta, alpha, low beta, high beta, and low gamma bands may be different. In some embodiments, additional signal groups may exist between exemplary frequency bands. The ADDS1700 can enable the configuration of frequency bands that become part of a signal group. The ADDS1700 extracts the signal by frequency band by filtering and removing signals that do not fall into one of the selected frequency bands.

[0191] In step 2032, the ADDS1700 can determine the level of connectivity between two brain regions based on a measure of the dependency between signals from those two brain regions, for each frequency band. For example, the ADDS1700 can calculate the connectivity value by measuring the phase synchronization between signals grouped by frequency band. The measure of phase synchronization includes the phase lag index or phase lock value (PLV) and its derivative, the imaginary part of the PLV (iPLV), and the corrected imaginary part of the PLV (ciPLV). The ADDS1700 can use other different connectivity measures, including information theory measures such as mutual information or transfer entropy, Granger causality measures, and conventional measures such as correlation, cross-correlation, coherence, or phase gradient index. The ADDS1700 can measure the level of connectivity between two brain regions based on a metric derived from the TIC. In some embodiments, the dependency measure can be based on the connectivity between brain regions across different frequency bands.

[0192] In step 2033, the ADDS1700 can determine if the signal type matches the theta band signal frequency by filtering the theta band signal. If the answer is "NO", proceed to step 2038.

[0193] In step 2034, ADDS1700 can determine whether the first condition of the second task is met. If the answer to the question is "YES", proceed to step 2035. If the answer to the question is "NO", proceed to step 2056.

[0194] In step 2035, ADDS1700 can check whether the second condition of the second task is met. If the answer to the question is "NO", proceed to step 2037.

[0195] In step 2036, ADDS1700 can check whether the third condition of the second task is met. If the answer to the question is "YES", proceed to step 2037. If the answer to the question is "NO", proceed to step 2056.

[0196] In step 2037, ADDS1700 can verify whether the user's connectivity level calculated in step 2032 is lower than the connectivity level of the healthy control group. If the answer to the question is "YES", proceed to step 2070. ADDS1700 can retrieve the connectivity levels of the healthy control group stored in the population database 1730. ADDS1700 receives users of the control group type as input and stores the result in the control group 1731. ADDS1700 can compare connectivity levels by verifying whether the calculated connectivity level is lower than the range of possible connectivity levels for the healthy control group. Based on the connectivity levels of individuals identified as having ADHD, as assessed by method 2000, ADDS1700 can determine the range of values ​​for the connectivity level of the healthy control group.

[0197] In step 2038, the ADDS1700 can determine if the signal type matches the beta band signal frequency by filtering the beta band signal. If the answer is "NO", proceed to step 2049.

[0198] In step 2039, ADDS1700 can determine if the stimulus is from the first task. If the answer to the question is "NO", proceed to step 2044.

[0199] In step 2040, ADDS1700 can check whether the first condition of the first task is met. If the answer to the question is "NO", proceed to step 2048.

[0200] In step 2041, ADDS1700 can determine whether the second condition of the first task is met. If the answer to the question is "YES", proceed to step 2048. If the answer to the question is "NO", proceed to step 2056.

[0201] In step 2044, ADDS1700 can check whether the first condition of the second task is met. If the answer to the question is "YES", proceed to step 2045. If the answer to the question is "NO", proceed to step 2056.

[0202] In step 2045, ADDS1700 can check whether the second condition of the second task is met. If the answer to the question is "YES", proceed to step 2046. If the answer to the question is "NO", proceed to step 2047.

[0203] In step 2046, ADDS1700 can check whether the third condition of the second task is met. If the answer to the question is "YES", proceed to step 2048. If the answer to the question is "NO", proceed to step 2056.

[0204] In step 2047, ADDS1700 can check whether the third condition of the second task is met. If the answer to the question is "NO", proceed to step 2048. If the answer to the question is "YES", proceed to step 2056.

[0205] In step 2048, ADDS1700 can verify whether the user's connectivity level calculated in step 2032 is higher than the connectivity level of the healthy control group. If the answer to the question is "YES", proceed to step 2070. ADDS1700 can retrieve the connectivity levels of the healthy control group stored in the population database 1730. ADDS1700 can receive users of the control group type as input and store the result in the control group 1731. ADDS1700 can compare connectivity levels by verifying whether the calculated connectivity level is lower than the range of possible connectivity levels for the healthy control group. ADDS1700 can determine the range of values ​​for the connectivity level of the healthy control group based on the connectivity level of the person identified as having ADHD, as assessed by method 2000.

[0206] In step 2049, the ADDS1700 can determine if the signal type matches the alpha band signal frequency by filtering the alpha band signal. If the answer to the question is "YES", proceed to step 2050. If the answer to the question is "NO", proceed to step 2056.

[0207] In step 2050, ADDS1700 can determine if the stimulus is from the first task. If the answer to the question is "NO", proceed to step 2054.

[0208] In step 2051, ADDS1700 can check whether the first condition of the first task is met. If the answer to the question is "NO", proceed to step 2051.2. Otherwise, proceed to step 2052.

[0209] In step 2051.2, if the connectivity level of user 1760 calculated in step 2032 is stored in container A1, proceed to step 2055. Container A1 may be a data structure such as a list or array. In some embodiments, container A1 may persist on disk as a flat file or database. In step 2052, ADDS1700 may check whether the second condition of the first task is met. If the answer to the question is "NO", proceed to step 2052.2. Otherwise, proceed to step 2052.3.

[0210] In step 2052.2, if the user connectivity levels calculated in step 2032 are stored in container A2, proceed to step 2055. Container A2 may be a data structure such as a list or array. In some embodiments, container A2 may persist on disk as a flat file or database. Container A2 contains the alpha band connectivity values ​​between brain regions when the first condition of the first task is met and the second condition is not met.

[0211] In step 2052.3, the connectivity level for user 1760, calculated in step 2032, is stored in container A1.

[0212] In step 2053, ADDS1700 can check whether the third condition of the first task is met, namely whether it is the red vowel "E". If the answer to the question is "YES", proceed to step 2053.2. Otherwise, proceed to step 2055.

[0213] In step 2053.2, if the connectivity level of the user calculated in step 2032 is stored in container A3, proceed to step 2055. Container A3 may be a data structure such as a list or an array. In some embodiments, container A3 may be persisted on a flat file or a database disk. Container A3 includes the value of the connectivity in the alpha band between brain regions when the first condition, the second condition, and the third condition of the first task are satisfied, that is, when it is the red vowel "E".

[0214] In step 2054, ADDS1700 can check whether the first condition, the second condition, and the third condition of the second task are satisfied. If the answer to the question is "YES", proceed to step 2054.2. Otherwise, proceed to step 2056.

[0215] In step 2054.2, if the connectivity level of the user calculated in step 2032 is stored in container A4, proceed to step 2055. Container A4 may be a data structure such as a list or an array. In some embodiments, container A4 may be persisted on a flat file or a database disk. Container A4 includes the value of the connectivity in the alpha band between brain regions when the first condition, the second condition, and the third condition of the second task are satisfied, that is, when it is the red vowel "E".

[0216] In step 2055, ADDS1700 can check whether the connectivity level of the user calculated in step 2032 is lower than that of the healthy control group. If the answer to the question is "YES", proceed to step 2070. ADDS1700 can obtain the connectivity level of the healthy control group stored in the population database 1730. ADDS1700 can receive users of the control group type as input and save the result in the control group 1731. ADDS1700 can compare the connectivity levels by checking whether the calculated connectivity level is lower than the range of possible connectivity levels for the healthy control group. The range of values to be included in the connectivity level of the healthy control group can be determined based on the connectivity level of the person identified as having ADHD.

[0217] In step 2056, ADDS1700 can check whether all the connectivity values calculated in step 2032 have been confirmed for ADHD identification by executing steps 2033 to 2055. If the answer to the question is "YES", proceed to step 2057. If the answer to the question is "NO", proceed to step 2033 to check the next connectivity value.

[0218] In step 2057, ADDS1700 can obtain the values of the connectivity levels of containers A1 and A2 from steps 2051.2, 2052.2, and 2052.3. ADDS1700 can compare the changes in the connectivity levels. If the decrease in the connectivity level is smaller than the decrease in the connectivity level of the healthy control group, proceed to step 2070.

[0219] In step 2058, ADDS1700 can obtain the values of the connectivity levels of containers A3 and A4 from steps 2053.2 and 2054.2 respectively. ADDS1700 can compare the changes in the connectivity levels. If the variation in the connectivity level between the user's brain regions is different from the decrease in the connectivity level between the same regions of the healthy control group, proceed to step 2070.

[0220] In some embodiments, ADDS1700 can compare the variability of connectivity levels between two regions based on connectivity levels determined in steps 2033, 2038, and 2049 of Method 2000. ADDS1700 can calculate the probability of ADHD based on the difference in the variability of connectivity levels between a user of ADDS1700 and a healthy control group. ADDS1700 can collect multiple connectivity levels from the same user or a set of users and store those connectivity levels as data for a healthy control group 1731, or store users identified as having ADHD as user 1732. ADDS1700 can use the stored values ​​of connectivity levels to determine the variability of connectivity levels. In some embodiments, the variability of connectivity levels may include only connectivity levels observed for the same data under different conditions. For example, a user of ADDS1700 may be presented with the same data, then task rules, to determine the connectivity level for the same data across multiple tasks. In step 2060, ADDS1700 may mark users (e.g., user 1760) whose brain signals are assessed as having signs of ADHD in terms of connectivity levels. Once step 2060 is complete, ADDS1700 completes the execution of method 2000 (step 2099).

[0221] Figure 21 is a flowchart showing the operation of an exemplary method for detecting ADHD according to some embodiments of the present disclosure. For illustrative purposes, the steps of Method 2100 can be performed by ADDS1700. It will be understood that Method 2100 as shown can be modified to change the order of the steps and to include additional steps.

[0222] In step 2110, the ADDS1700 can provide the user (e.g., user 1760 in Figure 17) with stimuli that elicit a response consistent with a first task (e.g., task 1800 in Figure 18A). The ADDS1700 can select a set of tasks to test the user (e.g., user 1760 in Figure 17) for ADHD, which can satisfy the rules (e.g., rules 1820, 1830 of task 1800, rules 1875, 1880 of task 1850). The ADDS1700 can provide stimuli by displaying content that satisfies the rules of task 1800 on the user device (e.g., user device 1750 in Figure 17).

[0223] In step 2120, the ADDS1700 can acquire a first set of signals from brain regions. The ADDS1700 can acquire signals from different brain regions by collecting electrical charges from probes attached to the head of user 1760 being tested for ADHD. The ADDS1700 can acquire signals when the user responds to the stimulus provided in step 2110 by performing an action. Performing an action may include clicking a button or using a pointing device to select something displayed on a screen.

[0224] In step 2130, ADDS1700 may present the same stimulus as part of a second task (e.g., task 1850 in Figure 18B). ADDS1700 can select a task if the rules are satisfied by both the first and second tasks. ADDS1700 can select a task from task definition 1733 only if the same content satisfies different rules that require opposite user responses. For example, in task 1800, when the red letter "E" is presented as a stimulus, rules 1820 and 1830 are satisfied and the user is expected not to respond, but in task 1850, rules 1875 and 1880 are satisfied and the user is expected to respond.

[0225] In step 2140, the ADDS1700 can acquire a second set of signals from a brain region. Similar to step 2120, the ADDS1700 can acquire signals by collecting brain images or electrical charges from the user 1760's brain when the stimulus is displayed in step 2130. The signals can be generated as part of a response performed by the user 1760.

[0226] In step 2150, the ADDS1700 can assess the connectivity level using a first signal set and a second signal set. The ADDS1700 can assess the connectivity level by measuring signals from a specific area of ​​the brain (e.g., the dorsal attentional network (DAN) 1910 in Figure 19) and signals from other areas of the brain (e.g., the ventral attentional network (VAN) 1920 in Figure 19) to determine the amount of transmitted signals that indicate the connectivity level.

[0227] In step 2160, the ADDS1700 can determine variations in the coupling levels of the first and second signal sets. The ADDS1700 can calculate the coupling levels by providing multiple stimuli as part of the first and second tasks in steps 2110 and 2130, and can determine variations in the coupling levels by acquiring the signals of the user 1760's responses in steps 2120 and 2140.

[0228] In step 2170, ADDS1700 can identify ADHD by comparing the variability in the connectivity level with a known variability in the connectivity level. ADDS1700 can compare the variability in the connectivity level calculated in step 2160 with the variability in the connectivity level of a control group (e.g., control group 1731). ADDS1700 can obtain the variability in the connectivity level of control group 1731 from a previously generated performance metric 1734. In some embodiments, ADDS1700 may need to evaluate the variability in the connectivity level from a previously calculated connectivity level stored in the performance metric 1734. If the variability in the connectivity level of user 1760 differs from the variability in the connectivity level of control group 1731, ADDS1700 can indicate that user 1760 has ADHD. ADDS1700 may consider the variability in the connectivity level to be different if the ranges of the connectivity level values ​​are different. In some embodiments, the variability in the connectivity level is considered to be different if the ranges of values ​​do not overlap. ADDS1700 can save the results of attention deficits to database 1730 under user 1732. ADDS1700 completes the execution of method 2100 (step 2199) once step 2170 is complete.

[0229] Example 1 A non-transient computer-readable storage medium that includes instructions, which, when executed by at least one processor, cause at least one processor to perform an action for the automatic detection of attention deficit hyperactivity disorder (ADHD), wherein the action includes providing a user with one or more stimuli to activate a plurality of brain regions representing an attention network, the one or more stimuli displaying information having a first condition for eliciting a response and a second condition as an exception to the first condition; acquiring a plurality of signals from the plurality of brain regions for each of the one or more stimuli, each of the plurality of signals being accessed over a period of time, the period beginning when the information is displayed and ending when the user's response is captured; and detecting ADHD by evaluating the plurality of signals based on the level of connectivity in the attention network when each of the plurality of signals corresponds to one or more stimuli.

[0230] Example 2 A non-transient computer-readable storage medium of Embodiment 1, further comprising presenting an instance of data matching a first task, wherein the first task includes a first condition relating to the prediction of the active and silent states of the first reaction, and the second condition restricts the prediction of the active state of the first reaction.

[0231] Example 3 A non-transient computer-readable storage medium of Embodiment 2, further comprising the first reaction displaying on a screen information that satisfies first conditions regarding the prediction of the active and silent states of the first reaction, and waiting for any user-shared reaction for a threshold period.

[0232] Example 4 A non-transient computer-readable storage medium of Embodiment 2, wherein the first response is at least one of clicking a pointing device, pressing a button, or failing to take action at a time threshold.

[0233] Example 5 A non-transient computer-readable storage medium of Embodiment 2 that detects ADHD by evaluating multiple signals based on connectivity levels in an attention network, further comprising: filtering alpha-band oscillatory signals from two or more of the user's brain regions upon receiving a first response; using the alpha-band oscillatory signals to determine connectivity between two brain regions; and comparing the connectivity level between two brain regions of the user to the connectivity level of a healthy user control group when a first condition of the first task is met (active state) and a second condition is negative, wherein if the user's connectivity level is lower than that of a healthy user control group, it indicates that the user has ADHD.

[0234] Example 6 A non-transient computer-readable storage medium of Example 2, which detects ADHD by evaluating multiple signals based on connectivity levels within an attention network, further comprising: filtering alpha-band oscillatory signals from two or more of the user's brain regions upon receiving a first response; using the alpha-band oscillatory signals to determine connectivity between two brain regions; and comparing the connectivity level between two brain regions of the user to the connectivity level of a healthy user control group when the first condition of the first task is a silent state and the second condition is positive, and indicating that the user has ADHD if the user's connectivity level is lower than that of a healthy user control group.

[0235] Example 7 The operation further includes, when receiving a first response, filtering beta-band oscillation signals of two or more brain regions and determining the connectivity between two regions of the brain using the beta-band oscillation signals; and comparing the connectivity level between two regions of the user's brain with the connectivity level of a control group of healthy users when the first condition of the first task satisfies a silent state and the second condition is positive. When the connectivity level of the user is higher than the connectivity level of the beta-band oscillation signals of the control group of healthy users, it indicates that the user has ADHD. A non-transitory computer-readable storage medium of Example 2.

[0236] Example 8 The operation further includes, when receiving a first response, filtering alpha-band oscillation signals of two or more brain regions and determining the connectivity between two regions of the brain using the alpha-band oscillation signals; determining the connectivity level between two regions of the user's brain when the first condition of the first task satisfies a silent state and the first condition of the first task satisfies an active state; and comparing the decrease in the connectivity level of the user between when the first task satisfies a silent state and when the first task satisfies an active state with the decrease in the connectivity level of a control group of healthy users between when the first task satisfies a silent state and when the first task satisfies an active state. When the decrease in the connectivity level of the user is smaller than the decrease in the connectivity level of the control group of healthy users, it indicates that the user has ADHD. A non-transitory computer-readable storage medium of Example 2.

[0237] Example 9 Detecting ADHD by evaluating a plurality of signals based on the connectivity level within the attention network further includes combining the respective signal data of the plurality of signals. Combining the signal data includes determining an increase in the connectivity level compared to the existing connectivity level between regions among the plurality of regions, or a decrease in the connectivity level from the existing connectivity level of a region among the plurality of regions. A non-transitory computer-readable storage medium of Example 1.

[0238] Example 10 A non-transient computer-readable storage medium of Embodiment 1, wherein displaying information having the first condition includes displaying categories of text or graphics.

[0239] Example 11 The second condition of the non-transient computer-readable storage medium of Example 10 includes displaying categories of text or shapes in a specific color.

[0240] Example 12 A non-transient, computer-readable storage medium of Embodiment 1, further comprising displaying second information having a first condition that elicits a response, a second condition as an exception to the first condition, and a third condition as an exception to the second condition, where one or more stimuli.

[0241] Example 13 The non-transient computer-readable storage medium of Embodiment 12 further includes presenting an instance of data that matches a second task, wherein the second task includes a first condition relating to the prediction of the active and silent states of the second reaction, the second condition restricting the prediction of the active state of the second reaction, and the third condition being an exception to the second condition relating to the prediction of the silent state of the second reaction.

[0242] Example 14 A non-transient computer-readable storage medium of Embodiment 13, further comprising the second reaction displaying on a screen information that satisfies a first condition regarding the prediction of the active and silent states of the second reaction, and waiting for any user-shared input for a threshold period.

[0243] Example 15 A non-transient computer-readable storage medium of Example 13, wherein the third condition includes selecting a subcategory of a text or graphic category that satisfies the first condition, and displaying the text or graphic subcategory.

[0244] Example 16 A non-transient computer-readable storage medium of Example 13, wherein the second response is at least one of clicking a pointing device, pressing a button, or taking no action within a threshold time.

[0245] Example 17 A non-transient computer-readable storage medium of Example 13, wherein the operation, upon receiving a second response, filters beta-band oscillatory signals from two or more regions of the user's brain, uses the beta-band oscillatory signals to determine connectivity between two brain regions, and compares the level of connectivity between two brain regions of the user to the connectivity level of a control group of healthy users when the first condition of the second task is active, the second condition is negative, and the third condition is negative, or when the first condition of the second task is active, the second condition is positive, and the third condition is positive, and if the user's connectivity level is higher than that of a control group of healthy users, it indicates that the user has ADHD.

[0246] Example 18 A non-transient computer-readable storage medium of Example 13, wherein the operation, upon receiving a second response from the user, filters theta-band oscillatory signals from two or more regions of the user's brain, and uses theta-band oscillatory signals to determine connectivity between two regions of the brain, and compares the connectivity level between two regions of the user's brain to the connectivity level of a control group of healthy users when the first condition of the second task is active, the second condition is negative, and the third condition is negative, or when the first condition of the second task is active, the second condition is positive, and the third condition is positive, and if the signal data of the user's connectivity level is lower than the connectivity level of a control group of healthy users, it indicates that the user has ADHD.

[0247] Example 19 A non-transient computer-readable storage medium of Example 13, wherein the operation, upon receiving a second response from the user, filters alpha-band oscillatory signals from two or more regions of the user's brain and uses the alpha-band oscillatory signals to determine connectivity between two brain regions; determines a change in the connectivity level between two brain regions of the user when presenting an instance of data for the second task when the first condition of the first task is active and the second condition of the first task is positive, when presenting an instance of data for the first task when the first condition of the first task is active and the second condition of the first task is positive; and further includes comparing the change in the user's connectivity level with the change in the connectivity level of a control group of healthy users, wherein if the change in the user's connectivity level differs from the change in the connectivity level of a control group of healthy users, it indicates that the user has ADHD.

[0248] Example 20 A non-transient computer-readable storage medium of Example 13, comprising: filtering alpha-band, beta-band, and theta-band oscillatory signals from two or more regions of a user's brain upon receiving a first and second response; determining inter-brain connectivity using the alpha-band, beta-band, and theta-band oscillatory signals; and evaluating the probability of detecting ADHD based on comparisons of connectivity levels, decreases in connectivity levels, and variations in the user's inter-brain connectivity levels relative to the connectivity levels of a control group of healthy users, when the first and second conditions of the first task, and the first, second, and third conditions of the second task are varied.

[0249] Example 21 A computer-aided method for the automated detection of Attention Deficit Hyperactivity Disorder (ADHD), comprising the steps of: providing a user with one or more stimuli to activate a plurality of brain regions representing an attention network, wherein one or more stimuli display information having a first condition for eliciting a response and a second condition as an exception to the first condition; acquiring a plurality of signals from a plurality of brain regions for each of one or more stimuli, wherein each of the plurality of signals is accessed over a period of time, which begins when the information is displayed and ends when the user's response is captured; and detecting ADHD by evaluating the plurality of signals based on the connectivity level of the attention network when each of the plurality of signals corresponds to one or more stimuli.

[0250] Example 22 An attention deficit detection system comprising one or more memory devices for storing processor-executable instructions and one or more processors configured to execute instructions causing an attention deficit detection system to perform an action, the action comprising: providing stimuli to activate one or more brain regions representing an attention network, the one or more stimuli including displaying information having a first condition for eliciting a response and a second condition as an exception to the first condition; acquiring a plurality of signals from the plurality of brain regions for each of the one or more stimuli, the acquisition of signals such that each of the plurality of signals is accessed over a period of time, the period of time beginning when the information is displayed and ending when a user response is captured; and detecting ADHD by evaluating the plurality of signals based on the connectivity level of the attention network when one of the plurality of signals corresponds to one or more stimuli.

[0251] Example 23 An attention deficit detection system comprising one or more memory devices for storing processor-executable instructions and one or more processors configured to execute instructions causing an attention deficit detection system to perform an action, wherein the action includes receiving first signal data from a user; determining a first set of time series based on the first signal data, each of the first set of time series corresponding to a respective source location located within the user's cranial cavity; calculating a first correlation value for a pair of the first set of time series; calculating a first correlation value for the pair of first set of time series included in the determined first set of time series; generating a score based on the first correlation value, the score indicating that the patient has an attention deficit or cognitive impairment such as ADHD; and outputting the generated score.

[0252] [Table 4-1]

Table 4-2

Table 4-3

Claims

1. The device (140, 200), The device (140, 200) is configured to receive (410) first electroencephalogram or magnetoencephalogram data (first EEG / MEG data) (130) of a subject (110), The apparatus (140, 200) is configured to determine a first plurality of time series (420) based on the first EEG / MEG data, and each of the first plurality of time series corresponds to a source location located within the subject's cranial cavity (300). The apparatus (140, 200) is configured to calculate a first correlation value (430) for a first pair of time series, and the first pair of time series is included in the first plurality of time series determined. The device (140, 200) is configured to receive the second EEG / MEG data of the subject (110), The apparatus (140, 200) is configured to determine a second set of time series based on the second EEG / MEG data, and each of the second set of time series corresponds to a source location located within the subject's cranial cavity (300). The apparatus (140, 200) is configured to calculate a second correlation value for a second pair of time series, wherein the second pair of time series is included in the determined second plurality of time series, and the second pair of time series corresponds to the same respective source positions as the first pair of time series. The apparatus (140, 200) is configured to calculate a comparison value between the second correlation value and the first correlation value. The apparatus (140, 200) is configured to generate one or more scores (150) (440) based on the comparison value, and the one or more scores indicate that the subject has a cognitive disorder such as attention deficit hyperactivity disorder (ADHD), The device (140, 200) is configured to output (450) one or more of the generated scores. Device (140, 200).

2. The respective source locations of the first time series in the first pair of time series are located in the frontal lobe (310) of the subject (110), such as the middle frontal gyrus (MFG) (320) and / or inferior frontal gyrus (IFG) (330) of the cranial cavity (300). The apparatus (140, 200) according to claim 1, wherein the respective source locations of the second time series of the first pair of time series are located in the superior parietal lobe (SPL) (340) of the subject's cranial cavity.

3. The device (140, 200) is further configured to calculate a third correlation value for a third pair of time series, the third pair of time series is included in the first plurality of time series determined, and the device (140, 200) is further configured to generate a score based on the first correlation value and the third correlation value, and / or The respective source locations of the first time series in the third pair of time series are located in ventral attention network areas such as the temporoparietal junction (TPJ) (350) of the cranial cavity (300) of the subject (110), and the respective source locations of the second time series in the third pair of time series are located in dorsal attention network areas such as the superior parietal lobe (SPL) (340) of the cranial cavity of the subject. The apparatus according to claim 1 or 2 (140, 200).

4. The device (140, 200) is further configured to calculate a fourth correlation value for a fourth pair of time series, wherein the fourth pair of time series is included in the first plurality of time series determined, and the device (140, 200) is further configured to generate a score based on the first correlation value, the third correlation value and the fourth correlation value, and / or The respective source locations of the first time series in the fourth pair of time series are located in the ventral attention network region, such as the temporoparietal junction (TPJ) (350) of the subject (110)'s cranial cavity (300), and the respective source locations of the second time series in the fourth pair of time series are located in the ventral visual pathway region, such as the inferior temporal gyrus (ITG) (360) of the subject's cranial cavity. The apparatus (140, 200) according to claim 3.

5. The apparatus (140, 200) according to claim 1 or 2, wherein determining the first plurality of time series (420) includes filtering the first EEG / MEG data (130) such that only the first plurality of time series include a subset of all available time series, the subset including a pair of first time series, a pair of second time series, and a pair of third time series.

6. The apparatus (140) according to claim 1 or 2, An electroencephalogram (EEG / MEG) device (120) for measuring at least the first EEG / MEG data (130) of the subject (110), While the EEG / MEG device is measuring at least the first EEG / MEG data (130) of the subject, a task presentation device (160) for presenting one or more tasks to the subject, A system (100) equipped with [this].

7. The steps include receiving (410) first electroencephalogram or magnetoencephalogram data (first EEG / MEG data) (130) of a subject (110), A step (420) of determining a first plurality of time series based on the first EEG / MEG data, wherein each of the first plurality of time series corresponds to a source location located within the subject's cranial cavity (300), A step (430) of calculating a first correlation value for a first pair of time series, wherein the first pair of time series is included in the first plurality of time series determined, The steps include receiving the second EEG / MEG data of the subject (110), A step of determining a second plurality of time series based on the second EEG / MEG data, wherein each of the second plurality of time series corresponds to a source location located within the cranial cavity (300) of the subject (110), A step of calculating a second correlation value for a second pair of time series, wherein the second pair of time series is included in the determined second plurality of time series, and the second pair of time series corresponds to the same respective source positions as the first pair of time series, A step of calculating a comparison value between the second correlation value and the first correlation value, A step (440) of generating one or more scores (150) based on the comparison values, wherein the one or more scores indicate that the subject has a cognitive disorder such as attention deficit hyperactivity disorder (ADHD), Step (450) of outputting one or more of the generated scores, A computer implementation method (400) including the above.

8. The respective source locations of the first time series in the first pair of time series are located in the frontal lobe (310) of the subject (110), such as the middle frontal gyrus (MFG) (320) and / or inferior frontal gyrus (IFG) (330) of the cranial cavity (300). The method according to claim 7 (400), wherein the respective source locations of the second time series of the first pair of time series are located in the superior parietal lobe (SPL) (340) of the subject's cranial cavity.

9. The aforementioned computer implementation method (400) further includes: A step of calculating a third correlation value for a third pair of time series, wherein the third pair of time series is included in the first plurality of time series determined, A step of generating a score based on the first correlation value and the third correlation value, including and / or, The respective source locations of the first time series in the third pair of time series are located in ventral attention network areas such as the temporoparietal junction (TPJ) (350) of the cranial cavity (300) of the subject (110), and the respective source locations of the second time series in the third pair of time series are located in dorsal attention network areas such as the superior parietal lobe (SPL) (340) of the cranial cavity of the subject. The method according to claim 7 or 8 (400).

10. The aforementioned computer implementation method (400) further includes: A step of calculating a fourth correlation value for a fourth pair of time series, wherein the fourth pair of time series is included in the first plurality of time series determined, A step of generating a score based on the first correlation value, the third correlation value, and the fourth correlation value, including and / or, The respective source locations of the first time series in the fourth pair of time series are located in the ventral attention network region, such as the temporoparietal junction (TPJ) (350) of the subject (110)'s cranial cavity (300), and the respective source locations of the second time series in the fourth pair of time series are located in the ventral visual pathway region, such as the inferior temporal gyrus (ITG) (360) of the subject's cranial cavity. The method according to claim 9 (400).

11. The first step (420) of determining the plurality of time series is A step of filtering the first EEG / MEG data (130) such that only the first plurality of time series include a subset of all available time series. The subset includes the first pair of time series, the second pair of time series, and the third pair of time series. The method according to claim 7 or 8 (400).

12. An instruction that, when executed by a processor, performs the method (400) according to claim 7 or 8, A computer-readable storage medium that stores data.

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