Cross-Reference to Related Applications

[0001] This application claims priority from patent application number EP20383043.5 filed on 1 st December 2020.

Field of the invention

[0002] The present invention relates to methods for predicting behavioural addictions in subjects.

Background

[0003] Behavioural addictions consist of addictive behaviours in which the object of addition is a behaviour instead of a substance. Examples include gambling, sex, eating or buying and/or gratifying activities whose effect on the performer is comparable to that of drugs. In these cases, they are recognized as behavioural addictions in the general population, such as pathological gambling, compulsive buying or hobbyism. Behavioural addictions (BA) are characterized by difficulties to resist an impulse to perform a typically pleasurable activity that is finally harmful to the person or to others.

[0004] Some medical conditions predispose to the development of behavioural addictions. This is mainly associated with the use of dopamine agonists or dopamine replacement therapy, DRT. This is the case of behavioural addictions (BA) in Parkinson’s disease, frequently referred as Impulsive Compulsive Behaviours or Impulse Control Disorders (ICB or ICD), which rarely appear in untreated patients and are especially linked to the treatment with dopamine agonists, DA. Another example of the use of dopamine agonists is the treatment of restless legs syndrome, RLS, also known as Ekbom syndrome, which may be treated with two families of drugs, gabapentinoids and dopamine agonists. DA used for RLS are the same in the treatment of Parkinson’s disease (PD) patients, comprising pramipexole, ropinirole and rotigotine, and they are associated to ICD in RLS (Tippmann- Peikert et al., “M. H. Pathologic gambling in patients with restless legs syndrome treated with dopaminergic agonists”. Neurology 68, 301-303 (2007)). In other examples not related to neurology, prolactinomas and hyperprolactinemia may be treated with dopamine agonists which have been associated with the development of behavioural addictions (Bancos I, et al. “Impulse control disorders in patients with dopamine agonist-treated prolactinomas and non-functioning pituitary adenomas: a case-control study”. Clin Endocrinol (Oxf) 2014; 80: 863-8). In the field of psychiatry, some specific dopamine agonists, such as pramipexole, have shown efficacy against depression in the general population. Some antipsychotics frequently used for mania, schizophrenia and for other psychiatric disorders have a partial dopamine agonist effect. Aripiprazole is the most frequently used drug of this family. Aripiprazole has been associated with behavioural addictions in psychiatric populations (Etminan M et al. “Risk of Gambling Disorder and Impulse Control Disorder With Aripiprazole, Pramipexole, and Ropinirole: A Pharmacoepidemiologic Study”. J Clin Psychopharmacol 2017; 37: 102-4). W02016069058A1 discloses a method for cross-diagnostic identification and a treatment of neurologic features underpinning mental and emotional disorders. In the example of Parkinson’s disease and other neurological diseases with movement disorders, DA have a major role in disease treatment making behavioural additions a frequent complication. PD is characterized by movement impairment, notably bradykinesia, rigidity, tremor and gait impairment. As motor symptoms are mainly due to low levels of dopaminergic activity within the basal ganglia, they tend to respond well to dopamine replacement therapy, DRT. Non-motor symptoms, which entail a burden to PD patients, usually do not respond well to DRT: on the contrary, DRT may worsen or even causes some of the non-motor symptoms.

[0005] DRT may influence or induce the development of PD-BAs, specifically when DRT includes dopamine agonists, DA. However, the many patients exposed to DA do not develop BA and some patients who never received DA do develop the disorder.

[0006] To date, a recent review by Marinus et al. considered male gender and DA the only proven risk factors to develop BA because they were the only ones that were confirmed in prospective studies. A different study featuring a predictive model for BA is based on a confirmation of the genetic component of PD-BA. On the one hand, the usage of DA in PD patients is beneficial to offset their dopaminergic activity deficiency and have advantages over other DRT; on the other hand, DA use is limited by the associated risk of BA. The associated risk of BA usually leads the treating physicians to limit the dose or avoid the use of DA in order to prevent the occurrence of BA. There is, therefore, a need to find a balance between the use of DA to control motor symptoms and, at the same time, to prevent the development of BA in patients who might benefit from DA use.

Summary

[0007] In a first aspect of the present disclosure, there is provided a computer implemented method for predicting whether a subject is likely to develop a behavioural addiction (BA), through a neurophysiologic measurement of the subject in response to incentive processing or reward processing, the method comprising, the method comprising:

- providing at the computer one or more neurophysiologic measurements from the subject, the neurophysiologic measurements acquired during performance of a gambling task by the subject;

- capturing by the computer an event related measurement for a win (ERMW) and an event related measurement for a loss (ERML) from the neurophysiologic measurement;

- determining a measurement difference (DIFF) between the event related measurement for a win (ERMW) and the event related measurement for a loss (ERML);

- comparing the measurement difference (DIFF) to a predetermined threshold;

- predicting whether the subject is likely to develop a behavioural addiction on the basis of the comparison.

[0008] The methods of the present disclosure comprise predicting, by a computer, whether a subject is likely to develop a behavioural addiction (BA). BAs are behavioural disorders characterized by difficulties to resist an impulse to perform a typically pleasurable activity. The prediction is based on a neurophysiologic measurement of the subject. Examples of neurophysiologic measurements may include, for example, electroencephalograms obtained by electrophysiologic techniques, and/or magnetoencephalograms obtained by magneto physiologic techniques and/or brain scans obtained through magnetic resonance imaging devices or through nuclear medicine devices. In the present disclosure the prediction is estimated or evaluated in response to incentive processing. Incentive processing may comprise the response to positive stimuli (e.g. monetary wins) and negative stimuli (e.g. monetary losses), the ability to learn from reward, the anticipation of future rewards, and engagement in goal-directed behaviour towards rewards. In the methods of the present disclosure the incentive processing is evaluated through the analysis of the subject's neurophysiological response to a feedback presented during a gambling task. Such methods comprise providing at the computer one or more neurophysiologic measurements from the subject, the neurophysiologic measurements acquired during performance of a gambling task by the subject. A gambling task may be understood in the present disclosure as a task in which a subject is invited to choose between two or more possible bets or possible alternatives. The gambling task may include one or more wins and one or more losses. In such scenario, the neurophysiologic measurement may be understood as an indicator of the reward or punishment experienced by the subject during the gambling task. [0009] The methods of the present disclosure further comprise capturing by the computer at least one event related measurement for a win (ERMW) and at least one event related measurement for a loss (ERML) from one or more neurophysiologic measurements. An event related measurement may be understood in the present disclosure as the measured brain response that is the direct result of a specific meaningful sensory event. Such brain response may be measured after a variable time following the event, depending on the technique used for the measurement and depending on the type of event. Examples of event related measurement may include but are not limited to event related potentials obtained through electroencephalography (EEG), event related fields obtained through magnetoencephalography (MEG), cerebral perfusion obtained through magnetic resonance imaging (MRI) and cerebral perfusion obtained through nuclear medicine. The amplitude of an event related measurement is unrelated to whether the feedback comes after a right or wrong decision but depends on whether this decision led to a win or a loss. The event related measurement is considered to represent reward prediction error, since the neurophysiological response depends on the difference between the expected feedback and the actual feedback.

[0010] The methods of the present disclosure further comprise determining a measurement difference (DIFF) between the event related measurement for a win (ERMW) and the event related measurement for a loss (ERML) and comparing such measurement difference (DIFF) to a predetermined threshold. The difference between the event related measurements generated after wins and after losses (DIFF) is used by the methods of the present disclosure as a marker of incentive processing, and the prediction of whether the subject is likely to develop a behavioural addiction is based on the comparison.

[0011] Advantageously, the methods of the present disclosure allow anticipating the cases in which a subject may develop behavioural addictions in the future. In other words, the methods of the present disclosure contribute to identify patients at low or high BA risk in order to find a balance between the use of DA to control the treated disorder or its symptoms and, at the same time, to prevent the development of BA in patients. In particular cases where subjects are prescribed dopamine replacement therapy (DRT) the methods of the present disclosure may predict a likely development of behavioural addictions by such subjects. DRT is usually recommended to patients with dopaminergic deficit including restless legs syndrome, Parkinson’s disease (PD), other movements disorders, and in patients in which modifying dopaminergic activity might be beneficial comprising hyperprolactinemia, prolactinoma, depression, and psychiatric disorders in which antipsychotics acting as dopamine agonists might be used. PD is the second most common neurodegenerative disease after Alzheimer’s. The hallmark of PD is movement impairment, notably bradykinesia, rigidity, tremor and gait impairment. As motor symptoms are mainly due to low levels of dopaminergic activity within the basal ganglia, the PD patients tend to respond well to DRT. Despite the benefits of DRT, DRT may cause some impairment, such as behavioural addictions. This is why the methods of the present disclosure are advantageous for predicting the development of BA and in the specific case of DRT’s patients, the method may allow avoiding, changing or reconsidering the dose of dopamine agonist drugs which may have been recommended in order to avoid a potential development of BA.

[0012] The methods of the present disclosure are based on the premise that a strong reward is the initial force that drives drug experimentation which is the first step in the way to drug addiction and on that a high activity in the reward system may lead to drug addiction and may also be a critical factor in the development of behavioural addictions and PD-BA.

[0013] The methods of the present disclosure identify differences in incentive processing by means of the measurement difference (DIFF) in PD patients likely to develop BA prior to any behavioural disorder. The difference, DIFF, supports therefore a predictive model for PD-BA in the present disclosure. Incentive processing is assumed to take part in behavioural addictions and PD-BA in the present disclosure.

[0014] In some examples, the methods may comprise neurophysiologic measurements obtained through an electroencephalography (EEG) apparatus, and the event related measurement may be understood as an event related potential. The event related potential is an electrical wave that appears after a stimulus which represents a monetary win or loss. Although the stimulus might be dependent on a previous decision by the person evaluated, the event related potential of the present disclosure does not depend on whether the decision is right or wrong, it depends on the feedback. In the present disclosure the event related potential may therefore be considered the electrical expression of incentive processing. The event related potential may differ between wins and losses. In order to have a reliable measurement of the event related potential the subject may be asked to perform several of multiple choices that are followed by a feedback. An example embodiment may comprise the event related potential corresponding to the mean amplitude at an Fz electrode between 250 and 450 ms following a feedback presentation.

[0015] The EEG activity occurring after the reward may be averaged to compensate the noise and increase the signal. EEG provides better temporal resolution than other techniques such as functional magnetic resonance imaging, MRI. EEG may be simpler to apply to a subject performing a task than other techniques such as cerebral perfusion or magnetoencephalography. The temporal resolution of EEG usually is comprised within the order of milliseconds, other techniques such as MRI usually have lower temporal resolution; other techniques such as nuclear medicine may have temporal resolution much lower up to some minutes for the most widely available tracers. EEG measurements neither require the supply of magnetic field sources as other techniques, such as MRI, do, nor the usage of radioactive tracers nor a magnetically shielded room.

[0016] In some examples of the method of the present disclosure, the threshold depends on one or more of the following variables: an age of the subject, a gender of the subject, a l-dopa equivalent daily dose (LEDD), DA I- dopa equivalent daily dose (DA-LEDD), an age at which the subject was diagnosed with Parkinson’s Disease (PD), a time since the PD diagnosis, the years of education of the subject, a motor status of the subject, a PD stage of the subject, a daily function score, DA use, a cognitive status of the subject, an anxiety value, depression value, apathy value of the subject, impulsivity of the subject, risk taking, REM sleep behaviour disorder.

[0017] The threshold is used by the methods of the present disclosure to discriminate subjects with a risk of developing BA. Depending on the definition of such threshold, the sensitivity of the methods of the present disclosure may vary. The threshold may be defined by an equation considering features of the subjects. Some of said features may include an age of the subject, for example in years, a gender of the subject, a l-dopa equivalent daily dose (LEDD), DA l-dopa equivalent daily dose (DA-LEDD), an age at which the subject was diagnosed with Parkinson’s Disease (PD), a time since the PD diagnosis, the years of education of the subject, a motor status of the subject, a PD stage of the subject, a daily function score, DA use, a cognitive status of the subject, an anxiety value, depression value, apathy value of the subject, impulsivity of the subject, risk taking, REM sleep behaviour disorder. The features may influence the risk of developing BA. In some examples of the method of the present disclosure the threshold depends, specifically, on the following variables: age, gender, DA l-dopa equivalent daily dose, and l-dopa equivalent daily dose. Advantageously such variables allow obtaining a more accurate prediction.

[0018] In some examples of the method of the present disclosure the neurophysiologic measurements are obtained through a magnetoencephalography (MEG) apparatus, and the event related measurement is an event related field.

[0019] In some examples of the method of the present disclosure the neurophysiologic measurements are obtained through an electroencephalography (EEG) apparatus, and the event related measurement is an event related potential. Although EEG and MEG signals originate from the same neurophysiological processes, magnetic fields are less distorted than electric fields by the skull and scalp, which results in a better spatial resolution of the MEG. Whereas scalp EEG is sensitive to both tangential and radial components of a current source in a spherical volume conductor, MEG detects only its tangential components. Scalp EEG may, therefore, detect activity both in the sulci and at the top of the cortical gyri, whereas MEG is most sensitive to activity originating in sulci. EEG is, therefore, sensitive to activity in more brain areas, but activity that is visible in MEG may also be localized with more accuracy. Scalp EEG is sensitive to extracellular volume currents produced by postsynaptic potentials. MEG detects intracellular currents associated primarily with postsynaptic potentials because the field components generated by volume currents tend to cancel out in a spherical volume conductor. The decay of magnetic fields as a function of distance is more pronounced than for electric fields. Therefore, MEG is more sensitive to superficial cortical activity. [0020] In some examples of the method of the present disclosure the neurophysiologic measurements are obtained through a magnetic resonance apparatus, and the event related measurement is a change in cerebral perfusion of blood flow obtained through arterial spin labelling (ASL) or through blood oxygenation level dependent (BOLD) imaging. The time of acquisition of these examples may be longer than the examples comprising EEG techniques, since the BOLD response is slower and offers a lower temporal resolution.

[0021] In some examples of the method of the present disclosure the neurophysiologic measurements are obtained through a nuclear medicine apparatus, and the event related measurement is a change in cerebral perfusion of blood flow.

[0022] In some examples of the method where the neurophysiologic measurements are obtained through an electroencephalography (EEG) apparatus, and the event related measurement is an event related potential. As it is apparent, if the event related measurement is an event related potential, the measurement difference (DIFF) is a potential difference (DIFF). In such cases, the prediction is made on the basis that the subject is likely to develop a behavioural addiction if the potential difference (DIFF) is above a threshold, defined by the formula: wherein C is: 0 if the subject is of female gender or 0.39 if the subject is of male gender; and wherein age is expressed in years, DA LEDD and LEDD in hundreds of mg (dg). The threshold may be understood as having the same dimension of the potential DIFF, expressed in microvolts, pV.

[0023] In some examples of the method where the neurophysiologic measurements are obtained through an electroencephalography (EEG) apparatus, and the event related measurement is an event related potential, the prediction is made on the basis that the subject is not likely to develop a behavioural addiction if the potential difference (DIFF) is below a second threshold, wherein the second threshold is determined by the formula:

, , , , , (C + 4.2 * |DA LEDD\ + 0.01 * |LEDD| -1.29 - 0.017 * lagel). second threshold = - - ^{1 1 1 1} - _e_ L-

0.53 ’ wherein C is:

- 0 if the subject is of a female gender;

- 0.39 if the subject is of a male gender; and wherein age is expressed in years, DA LEDD and LEDD in hundreds of mg (dg) and the potential DIFF in microvolts, pV.

[0024] In a second aspect of the present disclosure, there is provided a system for predicting whether a subject is likely to develop a behavioural addiction, characterized in that the system comprises a processor configured to receive one or more neurophysiologic measurements from a neurophysiologic measuring apparatus and wherein the processor is configured to perform the methods of the present disclosure.

[0025] The systems of the present disclosure may comprise a first processor configured to present a gambling task to the subject. The systems of the present disclosure may further comprise a neurophysiologic measuring apparatus. A neurophysiologic measuring apparatus may be understood in the present disclosure as a device to perform a measurement of brain functioning which may comprise electrophysiologic recordings, voltage recordings, local field potentials, magneto physiologic recordings, magnetic resonance imaging or radioactive energy that is emitted from the patient’s brain and converted into an image of a region of or whole the brain.

[0026] In an example system may comprise a computer in communication to an EEG apparatus, which may also comprise the means for presenting the stimuli or gambling task to the subject and may register the EEG measurements. Relevant variables, such as age, gender, daily dose or a medicine may be inserted into the computer and registered therein. The registered data may be sent to a server, or a second computer, or to a server in the cloud and a prediction may be obtained. [0027] In some examples prospective information relating to the same EEG measurements or other clinical information may be asked to the subjects in the long term. In such examples, the threshold may be updated in order for the predictions to be more accurate.

[0028] In some examples, the systems of the present disclosure may comprise printing means and the prediction may be handled to the subjects, A doctor may base a decision on a treatment for the subject. For example, in hypothetical cases, a subject with a DRT prescription may have the daily dose lowered if he/she is prone to develop BA.

[0029] In a third aspect of the disclosure there is presented a non-transitory computer-readable medium storing executable instructions that, when executed by a processor, cause the processor to operate any of the methods of the present disclosure.

Brief Description of the Drawings

[0030] Fig. 1 is an example representation of a system according to the invention.

[0031] Fig. 2 is an example representation of a system according to the invention.

[0032] Fig. 3 shows event related potentials measured through an EEG system.

[0033] Fig. 4 shows a difference between the ERMW and the ERML for a plurality of subjects.

[0034] Fig. 5 shows the receiver operating characteristic (ROC) curves of two models, obtained by plotting the sensitivity against the specificity of such models.

[0035] Fig. 6 shows an example embodiment of a system according to the present disclosure.

Detailed Description

[0036] In the following description, specific details are set forth in order to provide a thorough understanding of the present invention. [0037] Figure 1 shows a system where a method of the present disclosure may be implemented. The implemented method in the example system of figure 1 predicts whether a subject (102) is likely to develop a behavioural addiction (BA), through a neurophysiologic measurement (103) of the subject (102) in response to incentive processing. The neurophysiologic measurement may be acquired during the performance of a gambling task (104) by the subject (102). The neurophysiologic measurement (103) is acquired by a processor (101 ) and optionally shown on a screen. The gambling task may be shown on a screen (104). The gambling task may be run by the processor (101 ) acquiring the neurophysiologic measurement (103) or by a different processor. In the example of figure 1 , the processors (101 ) are represented as personal computers, but processor may comprise any kind of processing unit with the ability to perform computing operations.

[0038] The gambling task of the example represented in figure 1 includes a task similar to the task proposed by Gehring and known in the neurophysiology field. The example comprises two or more alternatives, and two or more physical or virtual buttons to choose one of the alternatives. One or more seconds after the choice, each alternative, which may comprise “5”, “25”, or letters, or symbols, or other numbers, turns red or green. Some example embodiments may comprise a latency time shorter than one second, letting the skilled person assume that such latency time is almost instantaneous. If the chosen alternative turns green, then the amount indicated by the chosen alternative is added to the total amount awarded to the subject at the end of a series of trials. If the chosen alternative of the present example turns red, then the amount indicated is subtracted from the total amount. The alternative not chosen by the subject turns red or green at the same moment that the chosen alternative turns green or red. A second example embodiment of the gambling task comprises the presentation of multiple trials for betting between two numbers. In the example embodiment, the subject is asked to choose one of the two numbers by pressing a button. One second after the subject’s selection, the method of the example comprises changing the colour of the numbers in order to indicate a win or a loss. The example embodiment further comprises infrequent boost trials in which wins are amplified by a multiplicative factor previously to the acquisition of the event related measurement.

[0039] As previously said, the method implemented by the system of figure 1 comprises acquiring a neurophysiologic measurement (103). Examples of neurophysiologic measurements may include but are not limited to event related potentials obtained through electroencephalography (EEG), event related fields obtained through magnetoencephalography (MEG), cerebral perfusion obtained through magnetic resonance imaging (RMI) and cerebral perfusion obtained through nuclear medicine.

[0040] Figure 2 shows an example of a system of the present disclosure where two neurophysiologic measurements (206, 207) represent two averages of several electroencephalograms obtained by EEG during several trials of gambling performed by several subjects, wherein a first curve (206) represents the average of trials resulting in wins and a second curve (207) represents the average of trials resulting in losses. The EEG measurements from each one of the several subjects may be obtained by an Fz electrode (205). Figure 2 shows a schematic representation of electrodes in an electroencephalography (EEG) headset and the electrophysiologic signal of an electrode Fz (205). Other examples of neurophysiologic measurements include one or more magnetoencephalograms obtained by a magnetophysiologic techniques, one or more brain scans obtained through magnetic resonance imaging devices or through a nuclear medicine device. The method further comprises capturing by the computer or processor (201 ) at least one event related measurement for a win, ERMW, (208) and at least one event related measurement for a loss, ERML, (209) from the neurophysiologic measurement acquired during the gambling task (204). In figure 2, ERMW (208) is a peak of potential measured after a time comprised between 250 milliseconds, ms, and 450 ms from an event, where the event comprises a win. In figure 2, ERML is a peak of potential measured after a time comprised between 250 ms and 450 ms from an event, where the event comprises a loss. In some cases, eye movements of the subject playing the gambling task may add some type of noise to the EEG measurements due to the fact that some electrodes may measure eye activity which may be superimposed to the cerebral activity. For avoiding such noise or artifacts in the measurements, an example embodiment may include the acquisition of vertical and horizontal eye movements using two additional bipolar channels which guarantees artifact minimization and rejection. An example embodiment may use Second Order Blind Inference (SOB I) to correct for eye movements. Examples include impedances of recording sites lower than 5 KO and signals filtered with a bandpass filter of 0.1-35 Hz and digitized at a rate of 250 Hz. In some example embodiment, the method comprises filtering the EEG signal in order to remove artifacts such as muscular activity, blinking and surrounding electromagnetic fields coming from alternate current power sources and electrical wires.

[0041] The method further comprises determining a measurement difference (DIFF) between the event related measurement for a win (ERMW) (208) and the event related measurement for a loss (ERML) (209). The method further comprises comparing the measurement difference (DIFF) to a predetermined threshold. The method further comprises predicting whether the subject is likely to develop a behavioural addiction on the basis of the comparison.

[0042] In the example embodiment shown in figure 2, the event related potential may be understood as the mean amplitude at Fz electrode (205) between 250 and 450 ms following the presentation of the gambling task, the subject is likely to develop a behavioural addiction if the potential difference (DIFF) is above a threshold, and the threshold is determined by the formula:

. . . (C + 4.2 * |DA LEDD\ + 0.01 * \LEDD\ +0.656- 0.017 * lagel) threshold =

0.53 ’ wherein C is: 0 if the subject is of female gender or 0.39 if the subject is of male gender; the age is expressed in years and the doses of DA LEDD and LEDD are expressed in dg. The threshold may be understood as having the same dimension of the potential DIFF, expressed in microvolts, pV. [0043] The formula of the present disclosure may be understood as dimensionless, wherein the values of DA l-dopa equivalent daily dose, and I - dopa equivalent daily dose are expressed as the absolute values of the dose in dg. Other examples may comprise the mean amplitudes of other electrodes, the mean amplitudes for a combination of electrodes, the mean amplitudes calculated throughout another temporal window, the peak amplitude at Fz electrode (205) measured throughout 250 and 450 ms following the presentation of the gambling task or throughout another temporal window. Further examples may comprise the peak amplitude throughout any temporal window.

[0044] An example embodiment may further comprise predicting whether the subject is not likely to develop a behavioural addiction if the potential difference (DIFF) is below a second threshold, wherein the threshold is determined by the formula: wherein C is:

- 0 if the subject is of a female gender;

- 0.39 if the subject is of a male gender;

- Age is expressed in years;

- doses of DA LEDD and LEDD are expressed in dg.

[0045] Figure 3 shows the event related potentials measured through an EEG system to subjects performing a gambling task. Figure 3 represents both the average ERMW (301 ) and the average ERML (302) of several trials of gambling performed by a first plurality of subjects with high risk of developing BA and the average ERMW (303) and the average ERML (304) of several trials of gambling performed by a second plurality of subjects with low risk of developing BA. Some examples may comprise the averages of ERMW and the averages of ERML for a plurality of subjects, for example, 100 subjects, or 200 subjects, or any number of subjects. The example of figure 3 shows that in a temporal window comprised between 250 ms and 450 ms following a win or a loss event, the difference between the ERMW and ERML is maximum both for subjects at high risk and low risk; in the case of subjects at high risk of developing BA, the difference DIFF is greater than the DIFF of subjects at low risk.

[0046] Figure 4 shows the difference, DIFF (401 ), between the ERMW and the ERML for a first plurality of subjects. Experiments have shown that 30 months after the execution of a method of the present disclosure, the first plurality of subjects had developed BA. In addition, Figure 4 shows the difference, DIFF (402), for a second plurality of subjects. Experiments have shown that, at least 30 months after the execution of a method of the present disclosure, the second plurality of subjects did not develop BA.

[0047] The DIFF is compared to a threshold by the methods of the examples shown in the figures. An example embodiment may comprise a threshold depending on one or more of the following variables: an age of the subject, a gender of the subject, a l-dopa equivalent daily dose (LEDD), DA I- dopa equivalent daily dose (DA-LEDD), an age at which the subject was diagnosed with Parkinson’s Disease (PD), a time since the PD diagnosis, the years of education of the subject, a motor status of the subject, a PD stage of the subject, a daily function score, DA use, a cognitive status of the subject, an anxiety value, depression value, apathy value of the subject, impulsivity of the subject, risk taking, REM sleep behaviour disorder. LEDD may be understood as the sum of l-dopa and the l-dopa equivalent dose of all the other dopaminergic drugs that a subject takes in a day. DA-LEDD may be understood as the sum of the l-dopa equivalent dose of all the drugs considered DA that a subject takes in a day. L-dopa, also known as levodopa, may be understood as the precursor to the neurotransmitters dopamine, norepinephrine, and epinephrine, which are collectively known as catecholamines. L-dopa may be administered with an inhibitor of peripheral metabolism and may be a main treatment for PD because it is the most effective way to increase brain dopamine. For each drug with dopaminergic effect used to treat PD an equivalency in l-dopa has been calculated or can be calculated. An example of daily function score may be understood as an assessment of the capabilities of people suffering from impaired mobility. The Schwab and England ADL (Activities of Daily Living) scale is a scale apt to assess such mobility capabilities. A cognitive status of the subject for the present disclosure may be understood as a cognitive assessment of the subject. An example of cognitive status may be obtained through The Parkinson's Disease-Cognitive Rating Scale PD-CRS, which is a cognitive screening scale that includes subtests to assess cortical and subcortical functions. An example of anxiety value may be understood as the detection of the state of anxiety. An example of depression value may be understood as the detection of the state of depression of the subject. In an example embodiment, the anxiety and depression values may be understood as results of a Likert questionnaire such as the Hospital Anxiety and Depression Scale, HADS. Apathy is defined as the lack of feeling, emotion, interest, concern, and behaviour recognition of goals. An example of apathy value of the subject may be understood as the result of a psychological tool for the assessment of apathy in subjects. A psychological scale usually used for the assessment of apathy in subjects with Parkinson’s disease is the Starkstein Apathy Scale. An example of impulsivity of the subject may be understood as the result of the assessment of the personality and/or behaviour of the subject. The Barratt Impulsiveness Scale BIS-11 is a questionnaire designed to assess the personality/behavioural construct of impulsiveness that can be employed in an example embodiment.

[0048] An example embodiment comprises a threshold depending on one or more of the following variables: age, gender, DA l-dopa equivalent daily dose, and l-dopa equivalent daily dose. The example is represented in figure 5 showing the receiver operating characteristic (ROC) curve of a clinical- demographic model (501 ) employing only clinical and demographic data in the predictive model and the ROC curve of an improved model (502) of the present disclosure, adding DIFF to the variables of the clinical-demographic model (501 ). The sensitivity against the specificity is represented in figure 5. The sensitivity may be understood as the proportion of subjects who do develop BA that are correctly identified/predicted as likely to develop BA by predictive methods. The specificity may be understood as the proportion of subjects who do not develop BA that are correctly identified/predicted as not likely to develop BA by predictive methods. The curves show the sensitivity and the specificity of a prediction method using variables and a prediction method using such variables and DIFF using different potential thresholds. The example improved model (502) shown in figure 5 employs the following variables: age, gender, DA l-dopa equivalent daily dose, and l-dopa equivalent daily dose, as the clinical-demographic model (501 ) does. In addition to the values of clinical-demographic model (501 ), the example improved model (502) of figure 5 employs the variable DIFF. As shown, including DIFF among the variables for predicting BA development allows reaching greater area under the ROC curve than the case where DIFF is not included in such definition of threshold.

[0049] An example of a model including DIFF in the definition of a threshold was used in a prospective study aiming BA prediction recruiting a sample population of 92 PD patients. Among the 92, such model discriminates: patients not likely to develop BA, patients likely to develop BA and the patients with average risk. The patients whose DIFF is under the second threshold are not likely to develop BA. The prospective study has shown that such subjects whose DIFF is under the threshold have a risk of developing BA lower than 2% per year. The threshold in these cases has been determined by the formula: wherein C is: 0 if the subject is of female gender or 0.39 if the subject is of male gender; and wherein age is expressed in years, DA LEDD and LEDD in hundreds of mg (dg) and the potential DIFF in microvolts, pV.

[0050] The patients whose DIFF is over a first threshold are likely to develop BA. The prospective study has shown that such subjects whose DIFF is over the threshold have a risk of developing BA higher than 20% per year.

Such threshold is determined by the formula: wherein C is 0 if the subject is of a female gender or 0.39 if the subject is of a male gender; and wherein age is expressed in years, DA LEDD and LEDD in hundreds of mg (dg) and the potential DIFF in microvolts, pV.

[0051] The patients whose DIFF is comprised between the first threshold and the second threshold present a moderate or intermediate risk to develop BA. The prospective study has shown that such subjects whose DIFF is comprised between the first threshold and the second threshold have a risk about 5% per year of developing BA.

[0052] Examples including comparing DIFF with the first threshold and the second threshold above may lead to a prediction of whether the subject is likely to develop a behavioural addiction more precisely than using a single threshold. It may be understood that having a risk about 5% per year of developing behavioural addiction may correspond to having a risk of developing behavioural addiction comprised between 4% and 6% per year.

[0053] In some examples the prediction includes a signal indicating that a subject is likely to develop BA. In some examples the prediction includes a signal indicating that a subject is not likely to develop BA. In some examples the prediction includes a signal indicating that a subject is at moderate risk to develop BA. In some examples the prediction includes a text indicating that a subject is at moderate risk to develop BA or that the subject is likely to develop BA or that the subject is not likely to develop BA. The signal or text may be printed, or shown on a screen or display, or be sent to a server or cloud server where the signal or text can be stored. The signal or text may be downloaded by an interested party, for example, a doctor.

[0054] Figure 6 shows an example embodiment of a system according to the present disclosure in which the neurophysiologic measurements (603) of the subject (602) who has played a gambling task (604) are stored in a computer-readable storage medium (605) and uploaded to the processor (601 ). The computer-readable storage medium (605) may comprise instructions which, when executed by a computer (601 ), cause the computer (601 ) to carry out the steps of a method of the present disclosure. In examples, the instructions may be stored in the processor (601 ) and the computer- readable storage medium (605) may only store the neurophysiological measurements. The processor (601 ) of the present example is used for determining a difference, DIFF, between the event related measurement for a win, ERMW, and the event related measurement for a loss, ERML and comparing the difference, DIFF, to a predetermined threshold, in order to predict the development of a BA.

[0055] An example embodiment comprises neurophysiologic measurements obtained through a magnetoencephalography (MEG) apparatus, wherein the event related measurement may be understood as an event related field. The event related measurement of the present example embodiment is an event related field appearing in the subject when receiving a feedback during the performance of a gambling task.

[0056] An example embodiment may comprise neurophysiologic measurements obtained through a magnetic resonance apparatus, wherein the event related measurement is a change in cerebral perfusion of blood flow obtained through arterial spin labelling or through blood oxygenation level dependent (BOLD) imaging or an analogous perfusion measurement by magnetic resonance.

[0057] An example embodiment may comprise neurophysiologic measurements obtained through a nuclear medicine apparatus, wherein the event related measurement is a change in cerebral perfusion of blood flow.

[0058] In some examples, and in order to have a reliable measurement of the event related potential, a subject may perform several trials of gambling that are followed by a feedback. A set of EEG measurements occurring after the feedback, e.g. 1000 ms after the feedback, are averaged to compensate the noise and increase the signal to noise ratio. [0059] An example embodiment may further comprise printing the prediction of developing BA. In such a case, a subject may provide the prediction to a doctor who may avoid, update, or modify the DRT in view of the results. Other communication techniques may be used, such as uploading the prediction to a cloud server, or encoding and ciphering the prediction before sending them to a recipient over the network. Some examples include sending the encrypted recordings through a network, e.g. the internet, to a server.

[0060] The preceding description has been presented to illustrate and describe certain examples. Different sets of examples have been described; these may be applied individually or in combination, sometimes with a synergetic effect. This description is not intended to be exhaustive or to limit these principles to any precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is to be understood that any feature described in relation to any one example may be used alone, or in combination with other features described, and may also be used in combination with any features of any other of the examples, or any combination of any other of the examples.

[0061] For reasons of completeness, various aspects of the present disclosure are set out in the following numbered examples:

[0062] Example 1. A method for discrimination of a subject likely to develop a behavioural addiction, BA, comprises:

- providing at a computer an age of the subject;

- providing at the computer a gender of the subject;

- providing at the computer a l-dopa equivalent daily dose (LEDD),

- providing at the computer a dopamine agonist (DA) l-dopa equivalent daily dose (DA-LEDD);

- providing at the computer a difference DIFF, wherein the difference is a difference between an event related measurement for a win (ERMW) and an event related measurement for a loss (ERML) from a neurophysiologic measurement of a subject; wherein the neurophysiologic measurements are acquired during performance of a gambling task by the subject; discriminating by the computer whether a subject is likely to develop a behavioural addiction by a formula comprising combining DIFF and the age, the gender, the LEDD and the DA-LEDD of the subject.

[0063] Example 2. The method of the example 1 , is further characterised in that the discrimination is determined by the following formula:

T = —1.92 — 0.017 * age + 0.39 * (sex = male) + 4.2 * DA-LEDD + 0.1 * LEDD — 0.53 * DIFF wherein age is expressed in years, DA-LEDD and LEDD in decigrams, dg, and the DIFF potential in microvolts, pV.

[0064] The example 2 may allow discriminating subjects likely to develop BA with a probability lower than 3% per year and subjects likely to develop BA with a probability comprised between 4% and 6% per year. The example 2 may allow determining that subjects in the lower quintile (T<-2.5) had a 30 months cumulative incidence of 5.5%, or 1 .9 cases/100 subjects per year. The first cut-off yields a sensitivity of 94% and a specificity of 37%. Subjects in the highest quintile (T>-0.63) have a cumulative incidence of 50% or 20.6 cases 1 100 subjects per year. The second cut-off yields a sensitivity of 61 % and a specificity of 88%. The remaining subjects had a cumulative incidence of 14.3% or 5 / 100 subjects per year.

[0065] Example 3. An example system for discrimination of a subject likely to develop a behavioural addiction may comprise a server configured to receive and store one or more neurophysiologic measurements from a neurophysiologic measuring apparatus. The system of this example is configured to perform the steps:

- receiving an age of the subject; a gender of the subject; a l-dopa equivalent daily dose (LEDD), a dopamine agonist (DA) l-dopa equivalent daily dose (DA-LEDD);

- calculate a difference DIFF, wherein the difference is a difference between an event related measurement for a win (ERMW) and an event related measurement for a loss (ERML) from a neurophysiologic measurement of a subject; wherein the neurophysiologic measurements are acquired during performance of a gambling task by the subject;

- discriminating whether a subject is likely to develop a behavioural addiction by a formula comprising combining DIFF and the age, the gender, the LEDD and the DA-LEDD of the subject.

[0066] Example 4. The system of the example 3 further comprises:

- a receiver configured to receive the neurophysiologic measurements in the form of an encrypted stream information; a decrypter for decrypting the encrypted stream information through a network to the server.

[0067] Example 5. A system for updating a DRT for a subject comprises means for implementing the steps of any of the example methods 1 or 2 and comprising a system according to the systems of the examples 3 and 4, wherein updating the DRT for the subject comprises:

- discriminating whether the subject is likely to develop BA according to any one of the method examples 1 or 2;

- updating a DRT on the basis of the discrimination wherein updating comprises at least one of the following actions:

- increase a dose of dopamine agonist, DA, at least if the subject is not likely to develop BA;

- maintain the dose of dopamine agonist, DA, at least if the subject is likely to develop BA with low probability of development;

- reduce the dose of dopamine agonist, DA, at least if the subject is likely to develop BA with high probability of development.

[0068] The example 5 refers to subjects likely to develop BA with a low probability, wherein low is understood to be lower than 3% per year and the example 5 further refers to subjects likely to develop BA with a high probability, wherein high is understood to be higher than 20% per year. [0069] As the skilled person may understand, other requirements may be included in the update of a DRT. The update of DRT may depend on specific health conditions of the subjects.

[0070] For example, before deciding to increase the DA dose of the DRT, a medical doctor may consider whether an increase of DA implies improving other clinical aspects for a patient, and only in the case that the other clinical aspects are improved, then the medical doctor may then update the DRT.

[0071] For example, before deciding to decrease the DA dose of the DRT, a medical doctor may consider whether it would be necessary to replace the previous dose of DA by another drug.