Brain network activity

Abstract

A method for determining activity of at least one brain network, including: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one activity model of the activity of at least one brain network to generate an activity indication signal for activity of the at least one brain network, wherein the at least one activity model includes at least one model of activity of a salience network (SN) or at least one model of activity of a default mode network (DMN); determining activity of the at least one brain network during said predetermined time period or a portion thereof, based on the generated activity indication signal, wherein said at least one brain network comprises the SN or the DMN.

Description

[0001] BRAIN NETWORK ACTIVITY

[0002] RELATED APPLICATION / S

[0003] This application claims the benefit of priority under 35 USC § 119(e) of U.S. Provisional Patent Application No. 63 / 657,879, filed June 9, 2024, the contents of which are incorporated herein by reference in their entirety.

[0004] FIELD AND BACKGROUND OF THE INVENTION

[0005] The present invention, in some embodiments thereof, relates to determining activity of a brain network and, more particularly, but not exclusively, to determining activity of a brain network based on signals recorded from a subject brain.

[0006] Background art includes International Patent Application Publication No. WO2012104853A2, and International Patent Application Publication No. WO2021260697A1.

[0007] SUMMARY OF THE INVENTION

[0008] Some examples of some embodiments of the invention are listed below (it should be noted that one or more features of an example may be used in combination with one or more features of another example):

[0009] Example 1. A method for determining activity of at least one brain network, comprising: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one activity model of the activity of at least one brain network to generate an activity indication signal for activity of the at least one brain network, wherein the at least one activity model comprises at least one model of activity of a salience network (SN) or at least one model of activity of a default mode network (DMN); determining activity of the at least one brain network during the predetermined time period or a portion thereof, based on the generated activity indication signal, wherein the at least one brain network comprises the SN or the DMN.

[0010] Example 2. A method according to example 1, wherein the at least one activity model comprises at least two activity models including the at least one model of activity of a SN and the at least one model of activity of the DMN, and wherein the processing comprises processing the EEG signals using the at least two activity models to generate at least two activity indication signals comprising an activity indication signal of the SN and an activity indication signal of the DMN, and wherein the determining comprises determining activity of the SN and the DMN during the predetermined time period or a portion thereof, based on the at least two activity indication signals.

[0011] Example 3. A method according to any one of examples 1 or 2, wherein the processing comprises processing the measured EEG signals using the at least one model of SN activity to generate an activity indication signal for activity of the SN, and wherein the determining comprises determining activity of the SN during the predetermined time period based on the generated activity indication signal.

[0012] Example 4. A method according to example 3, wherein the determining comprises determining activity of at least two brain regions of the SN, wherein the at least two brain regions comprise an amygdala and at least one additional brain region selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, and a rostral prefrontal cortex.

[0013] Example 5. A method according to example 3, wherein the determining comprises determining activity of at least two brain regions of the SN.

[0014] Example 6. A method according to any one of examples 4 or 5, wherein determining activity of the at least two brain regions of the SN comprises, calculating at least one activity value which is a measure of central tendency of activity of the at least two brain regions.

[0015] Example 7. A method according to any one of examples 5 or 6, wherein the at least two brain regions of the SN interact with each other.

[0016] Example 8. A method according to any one of examples 5 to 7, wherein the at least two brain regions of the SN are cortical brain regions located at a brain cortex.

[0017] Example 9. A method according to any one of examples 5 to 7, wherein the at least two brain regions of the SN comprise at least one cortical brain region and at least one sub-cortical brain region.

[0018] Example 10. A method according to any one of examples 5 to 7, wherein the at least two brain regions of the SN comprise at least two sub-cortical brain regions.

[0019] Example 11. A method according to any one of examples 5 to 10, wherein the at least two brain regions of the SN comprise at least two brain regions selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala.

[0020] Example 12. A method according to any one of examples 1 or 2 , wherein the processing comprises processing the measured EEG signals using the at least one model of DMN activity to generate an indication of activity of the DMN, and wherein the determining comprises determining activity of the DMN during the predetermined time period based on the generated activity indication.

[0021] Example 13. A method according to example 12, wherein the determining comprises determining activity of at least two brain regions of the DMN, wherein the at least two brain regions comprise the ventral striatum and at least one additional brain region selected from a group comprising, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0022] Example 14. A method according to example 12, wherein the determining comprises determining activity of at least two brain regions of the DMN.

[0023] Example 15. A method according to any one of examples 13 or 14, wherein determining activity of the at least two brain regions of the SN comprises, calculating at least one activity value which is a measure of central tendency of activity of the at least two brain regions.

[0024] Example 16. A method according to any one of examples 14 or 15, wherein the at least two brain regions of the DMN interact with each other.

[0025] Example 17. A method according to any one of examples 14 to 16, wherein the at least two brain regions of the DMN are cortical brain regions located at a brain cortex.

[0026] Example 18. A method according to any one of examples 14 to 16, wherein the at least two brain regions of the DMN comprise at least one cortical brain region and at least one sub-cortical brain region.

[0027] Example 19. A method according to any one of examples 14 to 16, wherein the at least two brain regions of the DMN comprise at least two sub-cortical brain regions.

[0028] Example 20. A method according to any one of examples 14 to 18, wherein the at least two brain regions of the DMN comprise at least two nodes of, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, a hippocampus and the ventral striatum.

[0029] Example 21. A method according to any one of the previous examples, wherein the at least one activity model comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the at least one brain network, when the at least one brain network is activated.

[0030] Example 22. A method according to any one of the previous examples, wherein the at least one activity model is a predictor of blood oxygenation level dependent (BOLD) activity of the at least one brain network.

[0031] Example 23. A method according to any one of the previous examples, wherein the at least one activity model comprises a ridge regression model of the at least one brain network or a portion thereof. Example 24. A method according to any one of the previous examples, comprising diagnosing the subject with at least one disorder, based on the determined activity of the at least one brain network.

[0032] Example 25. A method according to any one of examples 1 to 23, wherein the subject is diagnosed with at least one disorder, and wherein the method comprises selecting a treatment for the at least one disorder based on the determined activity of the at least one brain network.

[0033] Example 26. A method according to any one of examples 1 to 23, wherein the subject receives at least one treatment for at least one disorder, and wherein the method comprising determining an efficacy of the treatment based on the determined activity of the at least one brain network.

[0034] Example 27. A method according to any one of examples 25 or 26, wherein the treatment comprises at least one of, drug treatment, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT), biofeedback or neurofeedback.

[0035] Example 28. A method according to any one of examples 24 to 26, wherein the at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder and / or Alzheimer’s disease (AD).

[0036] Example 29. A method for determining activity of at least two brain networks, comprising: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one first activity model of the activity of a salience network (SN) and at least one second activity model of the activity of a default mode network (DMN), to generate a first activity indication signal for the activity of the SN and a second activity indication signal for the activity of the DMN; determining activity of the SN and the DMN during the predetermined time period or a portion thereof, based on the first activity indication signal and the second activity indication signal; determining a relation between activity of the SN and the activity of the DMN during the predetermined time period or a portion thereof, based on the determined activity.

[0037] Example 30. A method according to example 29, comprising: diagnosing the subject with at least one disorder based on the determined activity and / or the determined relation. Example 31. A method according to example 29, wherein the wherein the subject is diagnosed with at least one disorder, and wherein the method comprising selecting at least one treatment for the at least one disorder based on the determined activity and / or the determined relation.

[0038] Example 32. A method according to example 29, wherein the subject is diagnosed with at least one disorder and receives at least one treatment for the at least one disorder, and wherein the method comprising determining an efficacy of the at least one treatment based on the determined activity and / or the determined relation.

[0039] Example 33. A method according to any one of examples 31 or 32, wherein the at least one treatment comprises at least one of, drug treatment, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT), biofeedback or neurofeedback.

[0040] Example 34. A method according to any one of examples 30 to 32, wherein the at least one disorder comprises a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder and / or Alzheimer’s disease (AD).

[0041] Example 35. A system for determining activity of at least one brain network, comprising: a memory, wherein the memory stores signals recorded from a subject brain using at least one electrode, and at least one model of activity of at least one brain network, wherein the at least one activity model comprises an activity model of a salience network (SN) or an activity model of a default mode network (DMN); a user interface; a control circuitry, wherein the control circuitry is configured to: measure EEG signals from the signals stored in the memory; process the EEG signals using the at least one activity model comprising the activity model of the salience network (SN) or the activity model of the default mode network (DMN), to generate an activity indication signal for the activity of the at least one brain network; determine activity of the at least one brain network based on the generated activity indication signal, wherein the at least one brain network comprises the SN or the DMN; signal the user interface to generate an indication with information about the determined activity. Example 36. A system according to example 35, wherein the at least one model of activity comprises the activity model of the SN and the activity model of the DMN; wherein the control circuitry is configured to process the EEG signals using the activity model of the SN and the activity model of the DMN to generate a first activity indication signal for SN activity, and a second activity indication signal for DMN activity, determine activity of the SN and the DMN based on the first activity indication signal and the second activity indication signal.

[0042] Example 37. A system according to any one of examples 35 or 36, wherein the control circuitry is configured to determine activity of at least two brain regions of the SN based on the generated activity indication signal, wherein the at least two brain regions comprise an amygdala and at least one additional brain region selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, and a rostral prefrontal cortex.

[0043] Example 38. A system according to any one of examples 35 or 36, wherein the control circuitry is configured to determine activity of at least two brain regions of the SN based on the generated activity indication signal, wherein the at least two brain regions are selected from a group including, anterior insula, dorsal anterior cingulate cortex, supramarginal gyrus, rostral prefrontal cortex, and the amygdala.

[0044] Example 39. A system according to any one of examples 35 or 36, wherein the control circuitry is configured to determine activity of at least two brain regions of the DMN based on the generated activity indication signal, wherein the at least two brain regions comprise the ventral striatum and at least one additional brain region selected from a group comprising, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0045] Example 40. A system according to any one of examples 35 or 36, wherein the control circuitry is configured to determine activity of at least two brain regions of the DMN based on the generated activity indication signal, wherein the at least two brain regions are selected from a group comprising, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0046] Example 41. A system according to any one of examples 35 to 40, wherein the memory stores clinical and / or medical information on the subject, and wherein the control circuitry is configured to generate a diagnosis indication indicating a diagnosis of the subject with at least one disorder based on the clinical and / or medical information and based on the determined activity of the at least one brain network.

[0047] Example 42. A system according to any one of examples 35 to 40, wherein the memory stores information about at least one disorder of the subject, and a plurality of treatments for the at least one disorder, and wherein the control circuitry is configured to select at least one treatment out from the plurality of treatments based on the stored information and the determined activity of the at least one brain network. Example 43. A system according to any one of examples 35 to 42, wherein the memory stores information about at least one treatment provided to the subject, and wherein the control circuitry is configured to determine an efficacy of the treatment based on the stored information and the determined activity of the at least one brain network.

[0048] Example 44. A system according to any one of examples 41 or 42, wherein the at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder and / or Alzheimer’s disease (AD).

[0049] Example 45. A system according to any one of examples 42 or 43, wherein the at least one treatment comprises least one of, drug treatment, neurofeedback, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT).

[0050] Example 46. A system according to example 41, wherein the control circuitry is configured to signal the user interface to generate a human detectable indication with information about the diagnosis indication.

[0051] Example 47. A system according to example 42, wherein the control circuitry is configured to signal the user interface to generate a human detectable indication with information about the selected treatment.

[0052] Example 48. A system according to example 43, wherein the control circuitry is configured to signal the user interface to generate a human detectable indication with information about the determined efficacy.

[0053] Example 49. A system according to any one of examples 35 to 48, wherein the at least one model of activity stored in the memory comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the at least one brain network, when the at least one brain network is activated.

[0054] Example 50. A system for determining activity of at least one brain network, comprising: a memory, wherein the memory stores signals recorded from a subject brain using at least ne electrode, at least two activity models comprising at least one activity model of a salience network (SN) and at least one activity model of the default mode network (DMN); a control circuitry, wherein the control circuitry is configured to: measure EEG signals from the signals stored in the memory; process the EEG signals using the at least two activity models to generate at least two activity indication signals comprising a first activity indication signal for the activity of the SN and a second activity indication signal for the activity of the DMN; determine activity of the SN based on the first activity indication signal and determining activity of the DMN based on the second activity indication signal; generate an indication with information about the activity of the SN and activity of the DMN.

[0055] Example 51. A system according to example 50, wherein the determined activity comprises simultaneous activity of the SN and the DMN.

[0056] Example 52. A system according to any one of examples 50 or 51, wherein the at least one activity model of the SN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the SN or at least two brain regions of the SN, when the SN or the at least two brain regions of the SN are activated, and wherein the at least one activity model of the DMN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the DMN or at least two brain regions of the DMN, when the DMN or the at least two brain regions of the DMN are activated.

[0057] Example 53. A method for delivery of neurofeedback training, comprising: selecting at least one stimulus expected to activate at least one brain network in a subject brain, wherein the at least one brain network comprises a salience network (SN); delivering the at least one stimulus to the subject; recording electrical signals generated by the brain of the subject by at least one electrode, in conjunction with the delivering; processing the recorded electrical signals to determine an activation level of at least two brain regions of the SN; modifying the at least one stimulus according to the determined activation level and delivering the modified stimulus to the subject; repeating the delivering the recording, the processing, and the modifying.

[0058] Example 54. A method according to example 53, wherein the processing comprises processing the recorded electrical signal to determine an activation level of at least two brain regions of the SN, and wherein the modifying comprises modifying the at least one stimulus according to the determined activation level of the at least two brain regions of the SN.

[0059] Example 55. A method according to example 53, wherein the at least two brain regions are selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala.

[0060] Example 56. A method according to any one of examples 54 or 55, wherein the at least two brain regions comprise at least two cortical brain regions.

[0061] Example 57. A method according to any one of examples 54 or 55, wherein the at least two brain regions comprise at least one cortical brain region and at least one sub-cortical brain region. Example 58. A method according to any one of examples 53 to 57, wherein the processing comprises determining a relation between the activation level of the at least two brain regions in response to the at least one stimulus, and a baseline activation level of the at least two brain regions in response to at least one neutral stimulus selected not to activate the SN, and wherein the modifying comprises modifying the at least one stimulus according to the determined relation.

[0062] Example 59. A method according to any one of examples 53 to 58, wherein the subject is diagnosed with at least one disorder associated with irregular reactivity of the SN, and wherein the neurofeedback training is configured to teach the subject to modify the irregular reactivity of the SN towards regular reactivity of the SN.

[0063] Example 60. A method according to example 59, wherein the at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder and / or Alzheimer’s disease (AD).

[0064] Example 61. A system for delivery of neurofeedback training, comprising: a memory, wherein the memory stores at least one stimulus selected to activate a salience network (SN) in a subject, and at least one activity model of activity of the SN; a user interface; a control circuitry, wherein the control circuitry is configured to: signal the user interface to deliver the at least one stimulus to the subject; receive electrical signals recorded by at least one electrode from the subject brain during the delivery of the stimulus; measure EEG signals form the received electrical signals; process the EEG signals using the at least one activity model of the SN, to generate an activity indication signal for the activity of the SN; determine activity of the SN based on the generated activity indication signal; signal the user interface to modify the at least one stimulus according to the determined activity and to deliver the modified stimulus to the subject.

[0065] Example 62. A system according to example 61, wherein the control circuitry is configured to determine activity of at least two brain regions of the SN based on the generated activity indication signal, wherein the at least two brain regions comprise at least one cortical brain region and at least one sub-cortical brain region, or comprise at least two cortical brain regions. Example 63. A system according to example 62, wherein the at least two brain regions are selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala.

[0066] Example 64. A system according to any one of examples 61 to 63, wherein the at least one activity model of the SN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the SN, when the SN is activated.

[0067] Example 65. A system according to any one of examples 61 to 64, wherein the at least one activity model of the SN is a predictor of blood oxygenation level dependent (BOLD) activity of the SN.

[0068] Additional examples of some embodiments of the invention are listed below (it should be noted that one or more features of an example may be used in combination with one or more features of another example):

[0069] Example 1. A method for determining activity of at least one brain network, comprising: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one activity model of the activity of at least one brain network to generate an activity indication signal for activity of the at least one brain network, wherein the at least one activity model comprises at least one model of activity of a salience network (SN) or at least one model of activity of a default mode network (DMN); determining activity of the at least one brain network during the predetermined time period or a portion thereof, based on the generated activity indication signal, wherein the at least one brain network comprises the SN or the DMN.

[0070] Example 2. A method according to example 1, wherein the at least one activity model comprises at least two activity models including the at least one model of activity of a SN and the at least one model of activity of the DMN, and wherein the processing comprises processing the EEG signals using the at least two activity models to generate at least two activity indication signals comprising an activity indication signal of the SN and an activity indication signal of the DMN, and wherein the determining comprises determining activity of the SN and the DMN during the predetermined time period or a portion thereof, based on the at least two activity indication signals.

[0071] Example 3. A method according to any one of examples 1 or 2, wherein the processing comprises processing the measured EEG signals using the at least one model of SN activity to generate an activity indication signal for activity of the SN, and wherein the determining comprises determining activity of the SN during the predetermined time period based on the generated activity indication signal.

[0072] Example 4. A method according to example 3, wherein the determining comprises determining activity of at least two brain regions of the SN, wherein the at least two brain regions are selected from a group comprising, an amygdala, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, and a rostral prefrontal cortex.

[0073] Example 5. A method according to example 3, wherein the determining comprises determining activity of at least two brain regions of the SN, wherein the at least two brain regions of the SN are cortical brain regions located at a brain cortex , or wherein the at least two brain regions of the SN comprise at least one cortical brain region and at least one sub-cortical brain region, or wherein the at least two brain regions of the SN comprise at least two sub-cortical brain regions.

[0074] Example 6. A method according to any one of examples 4 or 5, wherein determining activity of the at least two brain regions of the SN comprises, calculating at least one activity value which is a measure of central tendency of activity of the at least two brain regions.

[0075] Example 7. A method according to any one of examples 1 or 2, wherein the processing comprises processing the measured EEG signals using the at least one model of DMN activity to generate an indication of activity of the DMN, and wherein the determining comprises determining activity of the DMN during the predetermined time period based on the generated activity indication.

[0076] Example 8. A method according to example 7, wherein the determining comprises determining activity of at least two brain regions of the DMN, wherein the at least two brain regions comprise the ventral striatum and at least one additional brain region selected from a group comprising, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0077] Example 9. A method according to example 7, wherein the determining comprises determining activity of at least two brain regions of the DMN, wherein the at least two brain regions of the DMN are cortical brain regions located at a brain cortex, or wherein the at least two brain regions of the DMN comprise at least one cortical brain region and at least one subcortical brain region, or wherein the at least two brain regions of the DMN comprise at least two sub-cortical brain regions. Example 10. A method according to any one of examples 8 or 9, wherein determining activity of the at least two brain regions of the SN comprises, calculating at least one activity value which is a measure of central tendency of activity of the at least two brain regions.

[0078] Example 11. A method according to any one of the previous examples, wherein the at least one activity model comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the at least one brain network, when the at least one brain network is activated.

[0079] Example 12. A method according to any one of the previous examples, wherein the at least one activity model is a predictor of blood oxygenation level dependent (BOLD) activity of the at least one brain network.

[0080] Example 13. A method according to any one of the previous examples, wherein the at least one activity model comprises a ridge regression model of the at least one brain network or a portion thereof.

[0081] Example 14. A method according to any one of the previous examples, comprising diagnosing the subject with at least one disorder, based on the determined activity of the at least one brain network.

[0082] Example 15. A method according to any one of examples 1 to 13, wherein the subject is prediagnosed with at least one disorder, and wherein the method comprises selecting a treatment for the at least one disorder based on the determined activity of the at least one brain network.

[0083] Example 16. A method according to any one of examples 1 to 13, wherein the subject receives at least one treatment for at least one disorder, and wherein the method comprising determining an efficacy of the treatment based on the determined activity of the at least one brain network.

[0084] Example 17. A method according to any one of examples 15 or 16, wherein the treatment comprises at least one of, drug treatment, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT), biofeedback or neurofeedback.

[0085] Example 18. A method according to any one of examples 14 to 16, wherein the at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

[0086] Example 19. A method for determining activity of at least two brain networks, comprising: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one first activity model of the activity of a salience network (SN) and at least one second activity model of the activity of a default mode network (DMN), to generate a first activity indication signal for the activity of the SN and a second activity indication signal for the activity of the DMN; determining activity of the SN and the DMN during the predetermined time period or a portion thereof, based on the first activity indication signal and the second activity indication signal; determining a relation between activity of the SN and the activity of the DMN during the predetermined time period or a portion thereof, based on the determined activity.

[0087] Example 20. A method according to example 19, wherein the determining a relation comprises determining a relation between amplitudes of the SN and DMN activity over a predetermined time period, between length of epochs in which activity of the DMN and SN changes, between magnitude of changes in the activity of the SN and DMN over a predetermined time period, and / or between any parameter of a first signal indicating SN activity and a second signal indicating DMN activity.

[0088] Example 21. A method according to any one of examples 19 or 20, comprising: diagnosing the subject with at least one disorder based on the determined activity and / or the determined relation.

[0089] Example 22. A method according to any one of examples 19 or 20, wherein the subject is prediagnosed with at least one disorder, and wherein the method comprising selecting at least one treatment for the at least one disorder based on the determined activity and / or the determined relation.

[0090] Example 23. A method according to any one of examples 19 or 20, wherein the subject is diagnosed with at least one disorder and receives at least one treatment for the at least one disorder, and wherein the method comprising determining an efficacy of the at least one treatment based on the determined activity and / or the determined relation.

[0091] Example 24. A method according to any one of examples 22 or 23, wherein the at least one treatment comprises at least one of, drug treatment, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT), biofeedback or neurofeedback.

[0092] Example 25. A method according to any one of examples 21 to 24, wherein the at least one disorder comprises a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD). Example 26. A system for determining activity of at least one brain network, comprising: a memory, wherein the memory stores signals recorded from a subject brain using at least one electrode, and at least one model of activity of at least one brain network, wherein the at least one activity model comprises an activity model of a salience network (SN) or an activity model of a default mode network (DMN); a user interface; a control circuitry, wherein the control circuitry is configured to: measure EEG signals from the signals stored in the memory; process the EEG signals using the at least one activity model comprising the activity model of the salience network (SN) or the activity model of the default mode network (DMN), to generate an activity indication signal for the activity of the at least one brain network; determine activity of the at least one brain network based on the generated activity indication signal, wherein the at least one brain network comprises the SN or the DMN; signal the user interface to generate an indication with information about the determined activity. Example 27. A system according to example 26, wherein the at least one model of activity comprises the activity model of the SN and the activity model of the DMN; wherein the control circuitry is configured to process the EEG signals using the activity model of the SN and the activity model of the DMN to generate a first activity indication signal for SN activity, and a second activity indication signal for DMN activity, determine activity of the SN and the DMN based on the first activity indication signal and the second activity indication signal.

[0093] Example 28. A system according to any one of examples 26 or 27, wherein the control circuitry is configured to determine activity of at least two brain regions of the SN based on the generated activity indication signal, wherein the at least two brain regions are selected from a group including, amygdala, anterior insula, dorsal anterior cingulate cortex, supramarginal gyrus, rostral prefrontal cortex, and the amygdala.

[0094] Example 29. A system according to any one of examples 26 or 27, wherein the control circuitry is configured to determine activity of at least two brain regions of the DMN based on the generated activity indication signal, wherein the at least two brain regions are selected from a group comprising, a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0095] Example 30. A system according to any one of examples 26 to 29, wherein the memory stores clinical and / or medical information on the subject, and wherein the control circuitry is configured to generate a diagnosis indication indicating a diagnosis of the subject with at least one disorder based on the clinical and / or medical information and based on the determined activity of the at least one brain network.

[0096] Example 31. A system according to example 30, wherein the control circuitry is configured to signal the user interface to generate a human detectable indication with information about the diagnosis indication.

[0097] Example 32. A system according to any one of examples 26 to 29, wherein the memory stores information about at least one disorder of the subject, and a plurality of treatments for the at least one disorder, and wherein the control circuitry is configured to select at least one treatment out from the plurality of treatments based on the stored information and the determined activity of the at least one brain network.

[0098] Example 33. A system according to example 32, wherein the control circuitry is configured to signal the user interface to generate a human detectable indication with information about the selected treatment.

[0099] Example 34. A system according to any one of examples 26 to 33, wherein the memory stores information about at least one treatment provided to the subject, and wherein the control circuitry is configured to determine an efficacy of the treatment based on the stored information and the determined activity of the at least one brain network.

[0100] Example 35. A system according to example 34, wherein the control circuitry is configured to signal the user interface to generate a human detectable indication with information about the determined efficacy.

[0101] Example 36. A system according to any one of examples 30 to 33, wherein the at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

[0102] Example 37. A system according to any one of examples 32 to 35, wherein the at least one treatment comprises least one of, drug treatment, neurofeedback, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT).

[0103] Example 38. A system according to any one of examples 26 to 37, wherein the at least one model of activity stored in the memory comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the at least one brain network, when the at least one brain network is activated.

[0104] Example 39. A system for determining activity of at least two brain networks, comprising: a memory, wherein the memory stores signals recorded from a subject brain using at least one electrode, at least two activity models comprising at least one activity model of a salience network (SN) and at least one activity model of the default mode network (DMN); a control circuitry, wherein the control circuitry is configured to: measure EEG signals from the signals stored in the memory; process the EEG signals using the at least two activity models to generate at least two activity indication signals comprising a first activity indication signal for the activity of the SN and a second activity indication signal for the activity of the DMN; determine activity of the SN based on the first activity indication signal and determining activity of the DMN based on the second activity indication signal; generate an indication with information about the activity of the SN and activity of the DMN.

[0105] Example 40. A system according to example 39, wherein the control circuitry is configured to determine a relation between at least one activity parameter of the determined SN activity and the at least one activity parameter of the determined DMN activity, wherein the at least one activity parameter comprises at least one of, amplitude, timing, and duration.

[0106] Example 41. A system according to any one of examples 39 or 40, comprising a user interface, and wherein the control circuitry is configured to signal the user interface to generate and deliver at least one human detectable indication indicating the determined activity of the SN and the determined activity of the DMN and / or the determined relation.

[0107] Example 42. A system according to any one of examples 39 to 41, wherein the at least one activity model of the SN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the SN or at least two brain regions of the SN, when the SN or the at least two brain regions of the SN are activated, and wherein the at least one activity model of the DMN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of the DMN or at least two brain regions of the DMN, when the DMN or the at least two brain regions of the DMN are activated.

[0108] Example 43. A method for delivery of neurofeedback training, comprising: selecting at least one sensory stimulus expected to activate at least one brain network in a subject brain, wherein the at least one brain network comprises a salience network (SN) and / or a default mode network (DMN); delivering the at least one sensory stimulus to the subject; recording electrical signals generated by the brain of the subject by at least one electrode, in conjunction with the delivering; processing the recorded electrical signals to determine an activation level of at least two brain regions of the SN and / or at least two brain regions of the DMN; modifying the at least one stimulus according to the determined activation level and delivering the modified stimulus to the subject; repeating the delivering the recording, the processing, and the modifying.

[0109] Example 44. A method according to example 43, wherein the at least two brain regions of the SN are selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala, and wherein the at least two brain regions of the DMN are selected from a group comprising wherein the at least two brain regions are selected from a group comprising, a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0110] Example 45. A method according to any one of examples 43 or 44, wherein the processing comprises processing the recorded electrical signals to determine a relation between the activation level of the at least two brain regions of the SN and / or of the DMN in response to the at least one stimulus, and a baseline activation level of the at least two brain regions of the SN and / or of the DMN in response to at least one neutral stimulus selected not to activate the SN and / or the DMN, and wherein the modifying comprises modifying the at least one stimulus according to the determined relation.

[0111] Example 46. A method according to any one of examples 43 to 45, wherein the subject is diagnosed with at least one disorder associated with irregular activity of the SN and / or of the DMN, and wherein the neurofeedback training is configured to teach the subject to modify the irregular activity of the SN and / or of the DMN towards a regular reactivity of the SN and / or of the DMN.

[0112] Example 47. A method according to example 46, wherein the at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

[0113] Example 48. A system for delivery of neurofeedback training, comprising: a memory, wherein the memory stores at least one sensory stimulus selected to activate a salience network (SN) and / or the default mode network (DMN) in a subject, and at least one activity model of activity of the SN and / or at least one activity model of activity of the DMN; a user interface; a control circuitry, wherein the control circuitry is configured to: signal the user interface to deliver the at least one stimulus to the subject; receive electrical signals recorded by at least one electrode from the subject brain during the delivery of the stimulus; measure EEG signals from the received electrical signals; process the EEG signals using the at least one activity model of the SN and / or using the at least one activity model of the SN, to generate a first activity indication signal for the activity of the SN and / or a second activity indication signal for the activity of the DMN; determine activity of the SN and / or activity of the DMN based on the generated activity indication signal; signal the user interface to modify the at least one stimulus according to the determined activity of the SN and / or of the DMN and to deliver the modified stimulus to the subject.

[0114] Example 49. A system according to example 48, wherein the control circuitry is configured to determine activity of at least two brain regions of the SN based on the generated first activity indication signal, and / or to determine activity of at least two brain regions of the DMN based on the generated second activity indication signal.

[0115] Example 50. A system according to example 49, wherein the at least two brain regions of the SN are selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala, or portions thereof, and / or wherein the at least two brain regions of the DMN are selected from a group comprising a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus, or portions thereof.

[0116] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

[0117] As will be appreciated by one skilled in the art, some embodiments of the present invention may be embodied as a system, method or computer program product. Accordingly, some embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro- code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, some embodiments of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. Implementation of the method and / or system of some embodiments of the invention can involve performing and / or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of some embodiments of the method and / or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware and / or by a combination thereof, e.g., using an operating system.

[0118] For example, hardware for performing selected tasks according to some embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to some embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to some exemplary embodiments of method and / or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and / or data and / or a non-volatile storage, for example, a magnetic hard-disk and / or removable media, for storing instructions and / or data. Optionally, a network connection is provided as well. A display and / or a user input device such as a keyboard or mouse are optionally provided as well.

[0119] Any combination of one or more computer readable medium(s) may be utilized for some embodiments of the invention. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0120] A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0121] Program code embodied on a computer readable medium and / or data used thereby may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0122] Computer program code for carrying out operations for some embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0123] Some embodiments of the present invention may be described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0124] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0125] Some of the methods described herein are generally designed only for use by a computer, and may not be feasible or practical for performing purely manually, by a human expert. A human expert who wanted to manually perform similar tasks, such as measuring electroencephalogram (EEG) signals, and / or processing of the EEG signals to determine activity of the salience network (SN) and / or the DMN network, as described herein, might be expected to use completely different methods, e.g., making use of expert knowledge and / or the pattern recognition capabilities of the human brain, which would be vastly more efficient than manually going through the steps of the methods described herein.

[0126] BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0127] Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings and images. With specific reference now to the drawings and images in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

[0128] In the drawings:

[0129] FIG. 1A is a flow chart of a general process for determining activity of a brain salience network (SN), according to some exemplary embodiments of the invention;

[0130] FIG. IB is a flow chart of a general process for determining activity of a brain default mode network (DMN), according to some exemplary embodiments of the invention;

[0131] FIG. 1C is a flow chart of a general process for determining activity of both the SN and the DMN in a subject, according to some exemplary embodiments of the invention; FIG. 2A is a schematic illustration of a brain showing nodes of a SN, according to some exemplary embodiments of the invention;

[0132] FIG. 2B is a schematic illustration of a brain showing nodes of a DMN, according to some exemplary embodiments of the invention;

[0133] FIG. 3 is a block diagram of a system for determining activation of at least one brain network comprising the SN and / or DMN, and / or delivering of a neurofeedback, according to some exemplary embodiments of the invention;

[0134] FIG. 4 is a flow chart of a detailed process for determining activation of the SN or the DMN, according to some exemplary embodiments of the invention;

[0135] FIG. 5A is a flow chart of a process for diagnosing a subject and selecting a treatment for the subject based on information on activity of the SN and / or DMN, according to some exemplary embodiments of the invention;

[0136] FIG. 5B is a flow chart of a process performed by a system, for diagnosing a subject and selecting a treatment for the subject based on information on activity of the SN and / or DMN, according to some exemplary embodiments of the invention

[0137] FIG. 6A is a flow chart of a process for determining treatment efficacy based on information on activity of the SN and / or DMN, according to some exemplary embodiments of the invention;

[0138] FIG. 6B is a flow chart of a process performed by a system for determining treatment efficacy based on information on activity of the SN and / or DMN, according to some exemplary embodiments of the invention;

[0139] FIG. 7 A is a flow chart of a process for determining activity of both the SN and the DMN by processing a single set of EEG signals using two different activity models, according to some exemplary embodiments of the invention;

[0140] FIG. 7B is a flow chart of a SN and / or DMN-based neurofeedback (NF) training, according to some exemplary embodiments of the invention;

[0141] FIG. 7C is a graph showing changes in two parameters of a feedback signal, optionally during a neurofeedback process, where a first parameter indicates activity of the SN, and a second parameter indicates activity of the DMN, according to some exemplary embodiments of the invention;

[0142] FIG. 8 is a schematic illustration of a methodological approach of a study;

[0143] FIGs. 9A and 9B are real-data illustrations showing BOLD activation of amygdala regions (9 A), and cortical regions (9B) associated with the amygdala-based EFP; FIG. 10 is a real-data illustration showing cortical distribution of BOLD signals associated with the amygdala-based EFP and of BOLD signals associated with the VS-based EFP;

[0144] FIGs. 11A and 11B are graphs showing mean effect size in major nodes of the salience network, for amygdala-based EFP and VS-EFP;

[0145] FIGs. 12A and 12B are real-data illustrations showing cortical regions of significant difference between BOLD signals associated with amyg-based EFP and BOLD signals associated with VS-EFP; and

[0146] FIGs. 13A-13C are real-data illustrations and graphs showing results of nonparametric testing of the distribution of voxel-wise correlations between the BOLD signal and amyg-based EFP signal within functional amygdala clusters.

[0147] DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

[0148] The present invention, in some embodiments thereof, relates to determining activity of a brain network and, more particularly, but not exclusively, to determining activity of a brain network based on signals recorded from a subject brain.

[0149] An aspect of some embodiments of the invention relates to determining activity of at least one brain network, for example at least one brain network comprising a salience network (SN) and / or a default mode network (DMN), based on signals recorded from a subject brain using at least one electrode. In some embodiments, determining of activity comprises calculating or measuring at least one activity value which is a measure of central tendency of activity of the at least one network, for example a mean, mode or median value of activity of the at least one brain network. In some embodiments, the at least one electrode comprises an electrode located outside a subject body, for example on a scalp of the subject. Alternatively, the at least one electrode comprises at least one electrode implanted at least partly or completely in a subject brain within the skull. In some embodiments, the activity of at least one network is determined based on EEG signals measured from the recorded signals. In some embodiments, the activity of both the SN and the DMN is determined based on the recorded signals, and optionally based on two distinct EEG data sets, each indicates activity of a different network.

[0150] According to some embodiments, the SN activity is determined based on selected EEG signals measured from the recorded brain signals that indicate SN activity. In some embodiments, the SN activity is determined by delivering a stimulation selected to affect activity of the SN, for example a stimulation selected to downregulate or upregulate the activity of the SN. In some embodiments, the SN is part of the limbic system and / or comprise regions of the limbic system. According to some embodiments, the SN activity is determined by applying a model which is a functional magnetic resonance imaging (fMRI)-guided EEG biomarker, also termed herein as an EEG-fMRI pattern (EFP) or an electrical finger print. In some embodiments, the SN EFP is a model of fMRI spatial scan data of an activated SN. In some embodiments, the SN EFP comprises an amygdala-based EFP generated by correlating EEG data with fMRI spatial scan data of the amygdala, for example as described in International Patent Application Publication Number WO2012104853A2, titled “method and system for use in monitoring neural activity in a subject's brain”, and in Meir-Hasson et al., 2013 titled “an EEG Finger-Print of fMRI deep regional activation”, both incorporated herein as a reference in their entirety. In some embodiments, the SN EFP comprises an amygdala-based ridge regression model.

[0151] According to some embodiments, the SN EFP is a predictor of a blood-oxygen-level- dependent (BOLD) signal of the SN. In some embodiments, the SN comprises two or more of, anterior insula (alns) brain region, dorsal anterior cingulate cortex (dACC) brain region, supramarginal gyrus (SMG) brain region, rostral prefrontal cortex (rPFC) brain region, and the amygdala brain region.

[0152] According to some embodiments, the activity of the SN is determined in order to diagnose a subject or as part of a diagnosis process of a subject suffering from one or more symptoms of a mental disorder. In some embodiments, a relation between an activity of the SN and a reference value, for example a previously determined SN activity in the same subject, a baseline activity of the SN in the subject, and / or an activity level of a group of subjects diagnosed with at least one specific mental disorder, is determined. In some embodiments, the subject is diagnosed based on the determined relation. Alternatively or additionally, the subject is diagnosed based on a determined relation between SN activity and DMN activity.

[0153] According to some embodiments, the activity of the SN is determined in order to predict, estimate or determine an efficacy of a treatment, for example a drug treatment, a psychotherapy treatment, a behavioral treatment, a cognitive treatment and / or a cognitive behavioral treatment, given to the subject.

[0154] According to some embodiments, the DMN activity is determined based on selected EEG signals measured from the recorded brain signals, that indicate DMN activity. In some embodiments, the DMN activity is determined by delivering a stimulation selected to affect activity of the DMN, for example a stimulation selected to downregulate or upregulate the activity of the DMN. In some embodiments, the DMN comprises regions of the reward system or correlates at least partly with the reward system. According to some embodiments, the DMN activity is determined by applying a model which is a functional magnetic resonance imaging (fMRI)-guided EEG biomarker also termed herein as an EEG-fMRI pattern (EFP) or an electrical finger print. In some embodiments, the DMN EFP is a model of fMRI spatial scan data of an activated DMN. In some embodiments, the DMN EFP comprises VS -based EFP generated by correlating EEG data with fMRI spatial scan data of the ventral striatum, for example as described in International Patent Application Publication Number WO2021260697A1, titled “ventral striatum activity”, and in Singer et al., 2023 titled “development and validation of an fMRI- informed EEG model of reward-related ventral striatum activation”, both incorporated herein as a reference in their entirety. In some embodiments, the DMN EFP comprises a VS -based ridge regression model.

[0155] According to some embodiments, the DMN EFP is a predictor of a blood-oxygen-level- dependent (BOLD) signal of the DMN. In some embodiments, the DMN comprises two or more of, a posterior cingulate cortex (PCC) brain region, a medial prefrontal cortex (mPFC) brain region, an inferior parietal lobe (IPL) brain region, a middle frontal gyrus (MFG) brain region, a middle temporal gyrus (MTG), a hippocampus (HP) brain region and the ventral striatum (VS) brain region.

[0156] According to some embodiments, the activity of the DMN is determined in order to diagnose a subject or as part of a diagnosis process of a subject suffering from one or more symptoms of a mental disorder. In some embodiments, a relation between an activity of the DMN and a reference value, for example a previously determined DMN activity in the same subject, a baseline activity of the DMN in the subject, and / or an activity level of a group of subjects diagnosed with at least one specific mental disorder, is determined. In some embodiments, the subject is diagnosed based on the determined relation.

[0157] According to some embodiments, the activity of the DMN is determined in order to determine an efficacy of a treatment, for example a drug treatment, a psychotherapy treatment, a behavioral treatment, a cognitive treatment and / or a cognitive behavioral treatment, given to the subject.

[0158] According to some embodiments, determining an activity of at least one network, of the SN and the DMN, and optionally generating an indication about the determined activity, may be used for at least one of, diagnosing a subject with a disorder, staging a disorder in a subject already diagnosed with the disorder, selecting a treatment to treat a disorder, determining an efficacy of a treatment already being delivered to the subject, and / or for providing information about the subject to an expert, for example a physician, a psychologist, a psychiatrist, or a therapist, optionally by classifying the determined activity of the SN and / or DMN. Optionally, the provided information is used to guide an interaction, for example a discussion, between the expert and the subject. Alternatively, or additionally, the provided information may help the expert to decide on a therapeutic approach for treating the subject.

[0159] According to some embodiments, a process for determining an activity of the SN and / or of the DMN, while measuring signals indicating activity of the SN and / or the DMN as described herein, is performed during a continuous time period of between 30 seconds and 12 hours, for example a time period of between 1 minute and 1 hour, a time period of between 10 minutes and 3 hours, a time period of between 1 minute and 60 minutes, or any intermediate, shorter or longer time period. In some embodiments, during this time period, signals indicating activity of the SN and / or the DMN are measured by at least one electrode.

[0160] According to some embodiments, the process for determining activity of the SN and / or of the DMN, is repeated every time period of between 30 minutes and 1 month, for example every 1 hour, every day, every week, every 2 weeks. Alternatively, the process is performed and / or repeated on-demand, for example prior to receiving a treatment, during a treatment and / or after competing a treatment. Alternatively or additionally, the process for determining activity of the SN and / or of the DMN, is performed as part of a subject diagnosis, for example before and / or during at least one diagnosis session. Alternatively or optionally, the process for determining activity of the SN and / or of the DMN is performed as part of a neurofeedback training configured to train a subject to modulate activity of one or both network.

[0161] According to some embodiments, as used herein, determining activity of the SN may comprise determining activity of at least two brain regions of the SN, for example at least two brain regions of the SN which comprise the Amygdala brain region and at least one additional brain region, or portions of the brain regions. In some embodiments, determining activity of the SN is used for identifying one or more affected constructs within a domain in a subject, for example constructs and domains as defined in the Research Domain Criteria (RDoC) matrix developed by the National Institute of Mental Health (NIMH), for example as described in Cuthbert, Bruce N; Insel, Thomas R (Dec 2013). "Toward the future of psychiatric diagnosis: the seven pillars of RDoC". BMC Medicine. 11 (1): 126, and in the website: en(dot)Wikipedia(dot)org / wiki / Research_Domain_Criteria, incorporated herein as a reference in their entirety.

[0162] According to some exemplary embodiments, determining activity of the SN is used to identify at least one affected construct of the domain Negative Valence System (VNS) in a subject, for example at least one construct of, acute threat (fear), potential threat (Anxiety), sustained threat, loss and / or frustrative nonreward. Alternatively or additionally, determining activity of the SN is used to identify at least one affected construct of the domain Arousal and Regulatory Systems in a subject, for example the construct arousal. Alternatively or additionally, determining activity of the SN is used to identify at least one affected construct of the domain Cognitive Systems, for example the constructs cognitive control / performance monitoring, and / or perception / Social communication, and / or attention. Alternatively or additionally, determining activity of the SN is used to identify at least one affected construct of the domain social processes, for example the constructs perception of others, and / or attachment.

[0163] According to some embodiments, as used herein, determining activity of the DMN may comprise determining activity of at least two brain regions of the DMN, for example at least two brain regions of the DMN which comprise the ventral striatum brain region and at least one additional brain region, or portions of the brain regions. In some embodiments, determining activity of the DMN is used for identifying affected constructs within a domain in a subject, for example constructs and domains as defined in the Research Domain Criteria (RDoC) matrix developed by the National Institute of Mental Health (NIMH), for example as described in Cuthbert, Bruce N; Insel, Thomas R (Dec 2013). "Toward the future of psychiatric diagnosis: the seven pillars of RDoC". BMC Medicine. 11 (1): 126, and in the website: en(dot)Wikipedia(dot)org / wiki / Research_Domain_Criteria, incorporated herein as a reference in their entirety.

[0164] According to some exemplary embodiments, determining activity of the DMN is used to identify at least one affected construct of the domain Positive Valence System (PNS) in a subject, for example at least one construct of, reward anticipation, initial responsiveness to reward attainment, reward learning, and / or habit formation. Alternatively or additionally, determining activity of the DMN is used to identify at least one affected construct of the domain Social Processes in a subject, for example the at least one construct, perception and understanding of self, perception and understanding of others and / or affiliation and attachment. Alternatively or additionally, determining activity of the DMN is used to identify at least one affected construct of the domain Cognitive Systems, for example the constructs cognitive control. Alternatively or additionally, determining activity of the DMN is used to identify at least one affected construct of the domain Negative Valence Systems, for example the constructs sustained threat, and / or loss.

[0165] An example of an affected construct is an impaired or dysregulated process in a subject, for example an impaired or dysregulated behavioral, cognitive, psychological and / or neurobiological process in the subject.

[0166] A potential advantage of identifying an affected construct of a specific domain in a subject may be to allow personalizing a treatment for a specific subject to specifically target the affected domain. An additional potential advantage of identifying an affected construct of a specific domain in a subject may be to allow better diagnosis and / or staging of a clinical state, for example a mental disorder, of the subject based not only on symptoms but also on affected functional circuits in the brain.

[0167] Additional potential advantages of identifying one or more affected constructs may be to allow transdiagnostic assessment of a subject across various domains, to allow dimensional analysis by promoting a graded understanding of functions and / or to allow integrative profiling of a subject clinical state.

[0168] Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and / or methods set forth in the following description and / or illustrated in the drawings and / or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

[0169] Exemplary determining SN activity

[0170] According to some exemplary embodiments, activity of at least one brain network, for example activity of the SN, is determined based on signals recorded from a subject brain by at least one electrode. In some embodiments, EEG signals are measured from the recorded signals. In some embodiments, the activity of the SN is determined based on a subset of EEG signals indicating SN activity.

[0171] Reference is now made to fig. 1A depicting a general process for determining activity of the SN, according to some exemplary embodiments of the invention.

[0172] According to some exemplary embodiments, signals are recorded from a subject brain, at block 102. In some embodiments, the signals are recorded by at least one electrode coupled to the subject body, for example at least one electrode coupled to the subject head. In some embodiments, the at least one electrode is positioned on a subject head, outside the skull. In some embodiments, the at least one electrode is positioned at one or more locations according to a 10- 20 coordinate system. In some embodiments, the at least one electrode comprises a plurality of electrodes, for example 2,3,4,5,6,7,8,9,10 or any larger number of electrodes.

[0173] According to some exemplary embodiments, the signals are recorded from the subject while delivering of at least one stimulus, optionally selected to activate the SN or a portion thereof, to the subject. In some embodiments, the stimulus is delivered to the subject as a visual and / or an audio sensory interface, for example an image, a sound, a movie, and / or as a virtual reality or an augmented reality interface. According to some exemplary embodiments, EEG signals are measured from the recorded signals, at block 104.

[0174] According to some exemplary embodiments, the EEG signals are processed at block 106. In some embodiments, the EEG signals are processed using at least one model of activity of the SN. In some embodiments, the SN model comprises an amygdala-based EFP. In some embodiments, the amygdala-based EFP, and the processing of EEG signals using the amygdalabased EFP is as described in International Patent Application Publication Number WO2012104853A2, titled “method and system for use in monitoring neural activity in a subject's brain”, and in Meir-Hasson et al., 2013 titled “an EEG Finger-Print of fMRI deep regional activation”, both incorporated herein as a reference in their entirety. In some embodiments, the processing comprises applying the SN model on the measured EEG and optionally generating an output signal indicating BOLD activity of the SN, for example indicating BOLD activity or optionally a central tendency measure of BOLD activity, of 2 or more brain regions of the SN. In some embodiments, the output signal indicates fMRI spatial scan data of an activated SN.

[0175] According to some exemplary embodiments, the activity of the SN is determined at block 108. In some embodiments, the activity of the SN in the subject from which the signals are recorded, is determined based on the results of the processing performed at block 106. In some embodiments, the activity of the SN is determined according to changes in the output signal, for example changes in the output signal over a predetermined time period. In some embodiments, the activity of the SN is determined based on a relation between the output signal or indication thereof and at least one reference value, for example previously determined SN activity, and / or baseline SN activity. In some embodiments, the activity of the SN is determined based on a relation between the output signal or indication thereof and a central tendency measure of SN activity in a group of subjects, for example, in healthy subjects, in subjects suffering from at least one symptom of a mental disorder, in subjects diagnosed with a mental disorder and / or in subjects receiving at least one treatment.

[0176] According to some exemplary embodiments, determining activity of the SN comprises calculating or measuring an activity value, optionally a central tendency measure value of activity of the SN. In some embodiments, determining activity of the SN comprises, determining activity of at least two brain regions of the SN, optionally interacting with each other. In some embodiments, the at least two brain regions comprise an amygdala and at least one additional brain region selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, and a rostral prefrontal cortex. In some embodiments, the at least two brain regions comprise at least two brain regions selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala.

[0177] According to some exemplary embodiments, the at least two brain regions comprise at least two cortical brain regions. Alternatively, the at least two brain regions comprise at least one cortical brain region and at least one sub-cortical brain region. Alternatively, the at least two brain regions comprise at least two sub-cortical brain regions. In some embodiments, determining activity of the SN comprises determining activity of the at least two brain regions of the SN. In some embodiments, the determining activity comprises calculating or measuring an activity value, optionally a central tendency measure of an activity value, indicating activity of the at least two brain region, optionally in a similar predetermined time period.

[0178] According to some exemplary embodiments, an indication is generated, at block 110. In some embodiments, the indication comprises an indication regarding the activity of the SN determined at block 108, optionally relative to a reference value, for example a previous determined activity in the same subject or a central tendency measure of activity as determined in a group of subjects, for example a group of subject having at least one characteristic similar to the subject. In some embodiments, the similar characteristic comprises a clinical and / or a personal characteristic, for example age, gender, and / or medical history.

[0179] Alternatively or additionally, the indication is generated based on the determined activity of the SN, and comprises information about the subject state, for example information about a cognitive, functional, behavioral, neurobiological and / or psychological state of the subject. Alternatively or additionally, the indication comprises information about a suggested diagnosis of the subject with a mental disorder, information about a suggested staging of an existing disorder in the subject and / or information about a suggested treatment for the subject.

[0180] According to some exemplary embodiments, the generated indication is optionally delivered to the subject and / or to an expert, at block 112.

[0181] According to some exemplary embodiments, the process described in blocks 102, 104, 106, 108 and 110 is repeated continuously over optionally a predetermined time period or during a time period adjusted automatically or manually. In some embodiments, the time period length is between 30 seconds and 3 hours, for example between 30 seconds and 5 minutes, between 2 minutes and 30 minutes, between 10 minutes and 60 minutes, or any intermediate, shorter or longer time period. Exemplary determining DMN activity

[0182] According to some exemplary embodiments, activity of at least one brain network, for example activity of the DMN, is determined based on signals recorded from a subject brain by at least one electrode. In some embodiments, EEG signals are measured from the recorded signals. In some embodiments, the activity of the DMN is determined based on a subset of EEG signals indicating SN activity.

[0183] Reference is now made to fig. IB depicting a general process for determining activity of the DMN, according to some exemplary embodiments of the invention.

[0184] According to some exemplary embodiments, signals are recorded from a subject brain, at block 120. In some embodiments, the signals are recorded by at least one electrode coupled to the subject body, for example at least one electrode coupled to the subject head. In some embodiments, the at least one electrode is positioned on a subject head, outside the skull. In some embodiments, the at least one electrode is positioned at one or more locations according to a 10- 20 coordinate system. In some embodiments, the at least one electrode comprises a plurality of electrodes, for example 2,3,4,5,6,7,8,9,10 or any larger number of electrodes.

[0185] According to some exemplary embodiments, the signals are recorded from the subject while delivering of at least one stimulus, optionally selected to activate the SN or a portion thereof, to the subject. In some embodiments, the stimulus is delivered to the subject as a visual and / or an audio sensory interface, for example an image, a sound, a movie, and / or as a virtual reality or an augmented reality interface.

[0186] According to some exemplary embodiments, EEG signals are measured from the recorded signals, at block 122.

[0187] According to some exemplary embodiments, the EEG signals are processed at block 124. In some embodiments, the EEG signals are processed using at least one model of activity of the DMN. In some embodiments, the DMN model comprises a ventral striatum (VS)-based EFP. In some embodiments, the VS-based EFP, and the processing of EEG signals using the VS-based EFP is as described in International Patent Application Publication Number WO2021260697A1, titled “ventral striatum activity”, and in Singer et al., 2023 titled “development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation”, both incorporated herein as a reference in their entirety. In some embodiments, the processing comprises applying the DMN model on the measured EEG and optionally generating an output signal indicating BOLD activity of the DMN, for example indicating BOLD activity of 2 or more brain regions of the DMN. In some embodiments, the output signal indicates fMRI spatial scan data of an activated DMN. According to some exemplary embodiments, the activity of the DMN is determined at block 126. In some embodiments, the activity of the DMN in the subject from which the signals are recorded, is determined based on the results of the processing performed at block 124. In some embodiments, the activity of the DMN is determined according to changes in the output signal, for example changes in the output signal over a predetermined time period. In some embodiments, the activity of the DMN is determined based on a relation between the output signal or indication thereof and at least one reference value, for example previously determined DMN activity, and / or baseline DMN activity. In some embodiments, the activity of the DMN is determined based on a relation between the output signal or indication thereof and a central tendency measure of DMN activity in a group of subjects, for example, in healthy subjects, in subjects suffering from at least one symptom of a mental disorder, in subjects diagnosed with a mental disorder and / or in subjects receiving at least one treatment.

[0188] According to some exemplary embodiments, determining activity of the DMN comprises calculating or measuring an activity value, optionally a central tendency measure value of activity of the DMN. In some embodiments, determining activity of the DMN comprises, determining activity of at least two brain regions of the DMN, optionally interacting with each other. In some embodiments, the at least two brain regions comprise a ventral striatum and at least one additional brain region selected from a group comprising, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus. In some embodiments, the at least two brain regions comprise at least two brain regions selected from a group comprising, a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

[0189] According to some exemplary embodiments, the at least two brain regions comprise at least two cortical brain regions. Alternatively, the at least two brain regions comprise at least one cortical brain region and at least one sub-cortical brain region. Alternatively, the at least two brain regions comprise at least two sub-cortical brain regions. In some embodiments, determining activity of the DMN comprises determining activity of the at least two brain regions of the SN. In some embodiments, the determining activity comprises calculating or measuring an activity value, optionally a central tendency measure of an activity value, indicating activity of the at least two brain region, optionally in a similar predetermined time period.

[0190] According to some exemplary embodiments, an indication is generated, at block 128. In some embodiments, the indication comprises an indication regarding the activity of the DMN determined at block 126, optionally relative to a reference value, for example a previous determined activity in the same subject or a central tendency measure of activity as determined in a group of subjects, for example a group of subject having at least one characteristic similar to the subject. In some embodiments, the similar characteristic comprises a clinical and / or a personal characteristic, for example age, gender, and / or medical history.

[0191] Alternatively or additionally, the indication is generated based on the determined activity of the DMN, and comprises information about the subject state, for example information about a cognitive, functional, behavioral, neurobiological and / or psychological state of the subject. Alternatively or additionally, the indication comprises information about a suggested diagnosis of the subject with a mental disorder, information about a suggested staging of an existing disorder in the subject and / or information about a suggested treatment for the subject.

[0192] According to some exemplary embodiments, the generated indication is optionally delivered to the subject and / or to an expert, at block 129.

[0193] According to some exemplary embodiments, the process described in blocks 120, 122, 124, 126 and 128 is repeated continuously over optionally a predetermined time period or during a time period adjusted automatically or manually. In some embodiments, the time period length is between 30 seconds and 3 hours, for example between 30 seconds and 5 minutes, between 2 minutes and 30 minutes, between 10 minutes and 60 minutes, or any intermediate, shorter or longer time period.

[0194] Exemplary determining DMN and SN activity

[0195] According to some exemplary embodiments, EEG signal measurements are used to determine activity of both the SN and the DMN, or portions thereof, in a subject. In some embodiments, the subject is already diagnosed with a disorder, and the measurements and determined activity are used to provide a more accurate diagnostics, for staging of a clinical state of the subject and / or to select or decide on a treatment for the subject. In some embodiments, the subject is already diagnosed and treated with a first treatment, and the measurements and determined activity are used to determine an efficacy of the first treatment and / or to decide whether to switch the first treatment with at least one second treatment. Alternatively, the subject is not pre-diagnosed, and the measurements and determined activity are used to diagnose the subject with a disorder, for example a mental disorder, or with a dysfunction.

[0196] Reference is now made to fig. 1C, depicting a process for determining activity of the DMN and SN, or portions thereof, optionally performed by a system, according to some exemplary embodiments of the invention. According to some exemplary embodiments, information about a subject is optionally received at block 130. In some embodiments, the information is received by a system, for example system 302 shown in fig. 3. In some embodiments, the information comprises personal information about the subject, medical history of the subject, results of one or more exams performed by the subject, for example questionnaires and / or input received from an expert, for example a psychiatrist, a psychologist, a therapist or a physician.

[0197] According to some exemplary embodiments, at least one stimulus is optionally delivered to the subject at block 132. In some embodiments, the at least one stimulus is a stimulus selected to affect, for example activate, the SN and the DMN. In some embodiments, the sat least one stimulus comprises at least two different stimuli, at least one for activating the DMN and at least one activating the SN. In some embodiments, the stimuli are delivered in a predetermined sequence to the subject, during a similar or a different time period.

[0198] According to some exemplary embodiments, signals are recorded from the subject brain, at block 134. In some embodiments, the signals are recorded, for example as described at blocks 102 and 120 in figs. 1A and IB, respectively. In some embodiments, the signals are recorded during and / or following the delivery of the at least one stimulus, at block 132.

[0199] According to some exemplary embodiments, EEG signals are measured, at block 136. In some embodiments, the EEG signals are measured based on the signals recorded at block 134. In some embodiments, the EEG signals are measured, for example as described at blocks 104 and 122 in figs. 1A and IB, respectively.

[0200] According to some exemplary embodiments, the EEG signals are processed at block 138. In some embodiments, the EEG signals are processed using a first activity model for the activity of the SN, and a second activity model for the activity of the DMN. In some embodiments, the EEG signals are processed, for example as described at blocks 106 and 124 in figs. 1A and IB, respectively. In some embodiments, for determining activity of the SN, one or more epochs of the measured EEG signals measured from signals recorded during and / or following delivery of a SN stimulus to the subject, are used. In some embodiments, for determining activity of the DMN, one or more epochs of the measured EEG signals measured from signals recorded during and / or following delivery of a DMN stimulus to the subject, are used.

[0201] According to some exemplary embodiments, activity of the SN and of the DMN is determined at block 140. In some embodiments, the activity is determined based on the results of the processing performed at block 138. In some embodiments, activity of the SN and of the DMN is determined, for example as described at blocks 108 and 126 in figs. 1A and IB, respectively. According to some exemplary embodiments, a score indicating a relation between the determined activity of the SN and the determined activity of the DMN is optionally calculated at block 142. In some embodiments, the calculated score indicates a ratio between the determined activity of the SN and the determined activity of the DMN in the subject.

[0202] According to some exemplary embodiments, an indication about the subject state is generated, at block 144. In some embodiments, the indication, optionally a human detectable indication, is generated at block 144. In some embodiments, the indication is generated based on the activity determined at block 144 and optionally based on the score calculated at block 142.

[0203] According to some exemplary embodiments, the indication comprises information about the determined activity of the SN and the DMN, and optionally comprises information on the score calculated at block 142. In some embodiments, the indication comprises information about a response of the subject to at least one treatment. Alternatively or additionally, the indication comprises a suggestion to replace the treatment with at least one different treatment, for example if the efficacy of the previous treatment is lower than a target efficiency level.

[0204] According to some exemplary embodiments, the indication comprises information about a suggested diagnosis of the subject. In some embodiments, optionally the indication about the suggested diagnosis is generated based on the information about the subject received at block 130.

[0205] According to some exemplary embodiments, the indication comprises information about a staging of a clinical state of the subject, optionally generated based on the information about the subject received at block 130.

[0206] According to some exemplary embodiments, the generated indication is optionally delivered to the subject and / or to an expert, at block 146.

[0207] According to some exemplary embodiments, the indication generated at block 144 includes information about the determined activity of the SN and / or the DMN, and / or information about the relation between activity of the SN and the DMN and / or the score calculated at block 142. In some embodiments, the indication is optionally delivered at block 146 as a human detectable indication, for example a visual and / or an audio indication.

[0208] Exemplary salience network (SN)

[0209] Reference is now made to fig. 2A, depicting at least some nodes of a salience network, according to some exemplary embodiments of the invention.

[0210] According to some exemplary embodiments, the SN is a brain network which comprises cortical nodes, for example an anterior insula (alns) 202 node, a dorsal anterior cingulate cortex (dACC) 204, supramarginal gyrus (SMG) 206, rostral prefrontal cortex (rPFC) 208, and at least one sub-cortical node, for example an amygdala 210. The SN and / or and least one of its central nodes play a role in one or more disorders presenting with symptoms of emotion and / or sensory dysregulation, for example, post-traumatic stress disorder (PTSD), chronic pain disorders such as Fibromyalgia, attention-deficit / hyperactivity disorder (ADHD), major depressive disorder (MDD) and other mood disorders, schizophrenia and psychosis, substance use disorders (SUD), and anxiety disorders.

[0211] According to some exemplary embodiments, the SN model, for example the amygdalabased EFP model is a model of activity of at least one, or two or more of the nodes of the SN, or sub-regions of the nodes. In some embodiments, the amygdala-based EFP model is a predictor of BOLD activity of at least two nodes of the SN comprising the amygdala 210 and at least one additional different node of the SN.

[0212] Exemplary default mode network (DMN)

[0213] Reference is now made to fig. 2B, depicting at least some nodes of a DMN, according to some exemplary embodiments of the invention.

[0214] According to some exemplary embodiments, the DMN is a brain network which comprises cortical nodes, for example a posterior cingulate cortex (PCC) 220, a medial prefrontal cortex (mPFC) 222, an inferior parietal lobe (IPL) 224, a middle frontal gyrus (MFG) 226, a medial temporal gyrus (MTG) 228, and at least one sub-cortical node, for example a hippocampus (HC) 230, and a ventral striatum (VS) 232. The DMN and / or and least one of its central nodes play a role in one or more mental disorders, for example, major depressive disorder (MDD), bi-polar disorder and other mood disorders, attention-deficit / hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), schizophrenia and psychosis, and substance use disorders (SUD).

[0215] According to some exemplary embodiments, the DMN model, for example the VS-based EFP model is a model of activity of at least one, or two or more of the nodes of the DMN, or subregions of the nodes. In some embodiments, the VS-based EFP model is a predictor of BOLD activity of at least two nodes of the DMN comprising the VS 232 and at least one additional different node of the DMN.

[0216] Exemplary system

[0217] According to some exemplary embodiments, a system for determining activity of at least one brain network, comprises at least one activity model of the at least one network, for example at least one model for SN activity and at least one model for DMN activity. In some embodiments, the system is configured to process measured signals, for example measured EEG signals using the at least one activity model to generate an output signal which indicates the activity of the model. In some embodiments, the system determines the activity of the at least one brain network based on the output signal. Alternatively, the system determined a relation, optionally by comparison, between the output signal and at least one reference stored in the memory of the system. In some embodiments, the system determines the activity of the at least one brain network based on the determined relation between the output signal and the at least one reference. For example, the system compares an outcome of the EEG processing, for example the outcome signal, to a database or to a distribution within a population of patients from previous procedures, which optionally share at least one characteristic feature with the subject from which signals are measured.

[0218] According to some exemplary embodiments, once the activity of the at least one brain network is determined, the system generates an indication, optionally a human detectable indication. In some embodiments, the generated indication is transmitted to a remote device and / or is delivered to a supervisor, for example a physician or any other expert. Alternatively or additionally, the generated indication is delivered to the subject from which the signals are measured.

[0219] Reference is now made to fig. 3 depicting a block diagram of a system for determining activity of the least one brain network, and / or for delivery of a neurofeedback, according to some exemplary embodiments of the invention.

[0220] According to some exemplary embodiments, a system 302 comprises a control unit 304, which is optionally a computer, a handheld device, or a mobile device. In some embodiments, the control unit 304 comprises a control circuitry 306 and a memory 308 in communication with the control circuitry 306. In some embodiments, the memory 308 stores at least one model for activity of at least one brain network, for example at least one model for activity of the SN, optionally a model for activity of at least two nodes of the SN, and / or at least one model for activity of the DMN, optionally a model for activity of at least two nodes of the DMN. In some embodiments, the memory 308 stores at least one algorithm, formula and / or a lookup table for processing signals, and / or for determining an activity of the at least one brain network.

[0221] According to some exemplary embodiments, the memory 308 comprises at least one reference value or at least one reference indication, indicating an activity of the at least one brain network in subject or in a group of subjects. It should be understood that in some embodiments the memory 308 can be any memory associated with the system 302, for example a memory of a remote device or a memory of any other device which is part of the system 302 or is in communication with control unit 304.

[0222] According to some exemplary embodiments, the control unit 304, for example the control circuitry 304, receives signals recorded by at least one electrode 316 coupled to a subject 311 body, for example to a head 318 of the subject 311. Optionally, the at least one electrode 316 is part of the system 302. In some embodiments, the at least one electrode 316 comprises two or more electrodes coupled to the subject scalp at one or more selected locations according to a 10- 20 coordinate system or a derivative thereof. In some embodiments, the at least one electrode 316 is configured and is positioned to record brain activity signals.

[0223] According to some exemplary embodiments, the control unit 304 comprises at least one user interface 310 functionally coupled to the control circuitry 306. In some embodiments, the user interface 310 is functionally coupled to least one display 314 and / or to at least one audio signals generator, for example a speaker 312. In some embodiments, in a session for determining activity of at least one brain network, the control circuitry 306 signals the user interface 310 to generate and deliver at least one sensory stimulus in a form of an audio and / or visual indication to the subject 311, optionally via the display 314 and / or the speaker 312. In some embodiments, the at least one sensory stimulus is stored in the memory 308, and is configured to affect the activity of the at least one network, for example the activity of the SN and / or the activity of the DMN. Optionally, the memory stores at least two distinct sensory stimuli, each one for selectively affecting a different brain network of the SN and the DMN. Alternatively, the same sensory stimulus is used to affect both the SN and the DMN.

[0224] Additionally or optionally, the memory 308 stores at least one neutral sensory stimulus selected not to affect the at least one brain network, for example to allow measurement of signals which indicate baseline activity of the at least one brain network.

[0225] According to some exemplary embodiments, the control circuitry 306 is configured to signal the user interface 310 to deliver a sensory stimulus, for example the activating sensory stimulus and / or the neutral sensory stimulus to the subject 311. In some embodiments, the control circuitry 306 receives recorded signals from the at least one electrode 316, recorded during the delivery of the sensory stimulus to the subject 311. In some embodiments, the recorded signals are stored in the memory 308.

[0226] According to some exemplary embodiments, the control circuitry 306 is configured to process the recorded signals during the session, for example an activity assessment session, or following the session. In some embodiments, the processing includes measurement of EEG signals from the recorded signals, optionally using at least one algorithm or formula stored in the memory 308. Additionally, the control circuitry 306 is configured to apply the at least one model for determining or estimating the activity of the at least one brain network using the measured EEG signals. In some embodiments, applying the model for determining or estimating the activity of the at least one brain network comprises selecting a specific subset of EEG signals, recorded by at least one selected electrode or a predetermined number of electrodes located as specific locations, decomposed into specific frequency band or range of frequency bands, and / or with a specific time delays, for determining or estimating the fMRI bold activity of the activated brain network. In some embodiments, the model is applied on EEG signals while signals are continuously recorded form the subject brain by the at least one electrode, for example to determine or estimate in real time the fMRI BOLD activity of the at least one activated brain network, during and in parallel to the signals recorded by the at least one electrode 316.

[0227] According to some exemplary embodiments, the control circuitry 306 is configured to determine the activity of the at least one brain network based on an output signal generated by the application of the model on the measured EEG signals. Optionally, the control circuitry 306 determines the activity based on a relation between the generated output signal and at least one reference, for example based on a determine relation between the output signal or activity calculated in response to the activating sensory stimulus, and an output signal or activity calculated in response to a neutral sensory stimulus. Alternatively or additionally, the control circuitry 306 determines the activity based on a relation between the generated output signal and at least one reference which comprises previously calculated activity in the same subject or in a group of subjects. In some embodiments, the determined activity is stored in the memory 308.

[0228] According to some exemplary embodiments, the control unit 304 comprises a communication circuitry 326, functionally coupled to the control circuitry 306. In some embodiments, the communication circuitry 326 is configured to communicate, for example to deliver and / or receive signals, with a different device, for example with a remote device 330 and / or with a supervisor device 328. In some embodiments, the communication circuitry 326 is configured to deliver and / or receive wireless signals or signals via wires, to and / or from the different device, respectively. In some embodiments, the control circuitry 306 is configured to generate an indication regarding the determined activity, for example a human detectable indication, and to signal the communication circuitry 326 to deliver the indication to the different device. Alternatively or additionally, the control circuitry 306 is configured to signal the user interface 310 to generate an indication with regard to the determined activity, and to deliver the indication to the subject 311, optionally via the display and / or the speaker 312. According to some exemplary embodiments, the indication with regard to the determined activity comprises at least one of, an indication about activity of the at least one brain network during the session, for example in response to the sensory stimulus, a suggestion how to modify a treatment already provided to the subject in view of the determined activity, a suggestion for a treatment of the subject in view of the determined activity, an indication about an efficacy of a treatment already provided to the subject, a diagnosis of a subject with at least one mental disorder, and / or an indication regarding a state or a stage of at least one mental disorder from which the subject suffers.

[0229] According to some exemplary embodiments, the remote device 330 is a device located at least one meter from the control unit 302, and optionally in a different room or building. In some embodiments, the remote device 330 comprises at least one of, a computer, a database, a remote server, a cloud storage, a processing cloud, a mobile device, a handheld device, and / or a cellular device. In some embodiments, the remote device 330 is configured to process signals received from the at least one electrode, and to generate an indication for activity of the at least one brain network, optionally similarly as the control circuitry 306 using information stored in a memory of the remote device 330 or in memory 308.

[0230] According to some exemplary embodiments, the remote device 330 and / or the control circuitry is configured to update one or more parameters of an assessment session or an assessment procedure, based on the determined activity. In some embodiments, the one or more parameters comprises, type and / or details of a stimulus presented to the subject, location of one or more electrodes, duration of an assessment session, duration of recording signals by the at least one electrode 316, type of activity model to be used, parameters of an activity model, and / or one or more steps of the determining activity process.

[0231] According to some exemplary embodiments, the remote device 330, for example a database, is configured to update at least one entry or a measure stored in the database, based on the activity of the at least one brain network in the subject 311, for example a central tendency measure of a activity of a group of subjects which include the subject 311.

[0232] According to some exemplary embodiments, the at least one electrode 316 comprises a plurality of electrodes configured to be positioned at selected locations on a subject scalp according to a 10-20 coordinate system or an extended coordinate system thereof, for example at two or more of the locations C3, C4, Cz, FCZ, P3, Pz, P4, F7, F8, T7, T8, P8, TP9 and TP10.

[0233] According to some exemplary embodiments, when system 302 or components thereof is used for delivery of neurofeedback, the memory 308 stores at least one sensory interface, optionally a sensory stimulus configured or selected to affect the activity of the SN and / or of the DMN. In some embodiments, the at least one sensory interface comprises two sensory interfaces each is configured to affect activation of a different network of the SN and DMN networks. Additionally, the memory stores at least one model, for example an EFP, of an activity of the SM, and / or at least one additional model of activity of the DMN.

[0234] According to some exemplary embodiments, during a neurofeedback session, the control circuitry signals a user interface of the system, optionally comprising the display 314 and / or the speaker 312, to deliver the at least one sensory interface, for example the at least one stimulus to the subject 311. In some embodiments, the control circuitry 306 is configured to generate and deliver to the subject an indication to apply at least one mental strategy, for example an exercise, to try and modulate the presented sensory interface into a desired sensory interface. Optionally, the indication is delivered to the subject 311 via the user interface of the system 302.

[0235] According to some exemplary embodiments, during the delivery of the sensory interface to the subject, the control circuitry receives electrical signals recorded by at least electrode, for example electrode 316, for the subject brain. In some embodiments, the control circuitry 306 measures EEG signals from the received electrical signals, optionally using at least one algorithm stored in the memory 308. In some embodiments, the control circuitry is configured to process the EEG signals using the at least one model of activity of the SN or of the DMN stored in the memory 308, to generate an activity indication signal of a network. Optionally, the control circuitry 306 processes the EEG signals using a model for activity of the SN and a model for activity of the DMN, and generates a first activity indication signal for the SN and a second activity indication signal for the DMN.

[0236] According to some exemplary embodiments, during the neurofeedback session while the sensory interface is delivered to the subject, the control circuitry 306 determines an activity of at least one network or both networks, based on the one or more activity indication signals. In some embodiments, optionally, determining activity of both the SN and of the DMN, comprises determining by the control circuitry 306 a relation between the activity of the SN and the activity of the DMN, or a relation between at least one parameter of activity of both networks. Optionally, the control circuitry 306 calculates a score based on the relation.

[0237] According to some exemplary embodiments, during the neurofeedback session, the control circuitry 306 signals the user interface 310 to deliver a feedback signal to the subject, by modulating the at least one sensory interface delivered to the subject, according to the determined activity of the SN, according to the determined activity of the DMN, according to the determined relation between activity of the SN and activity of the DMN or parameter of the activities, or according to the calculated score indicating a relation, optionally a specific relation. According to some exemplary embodiments, the control circuitry 306 is configured to determine activity of at least two brain regions of the SN based on the generated activity indication signal of the SN. Alternatively or additionally, control circuitry 306 is configured to determine activity of at least two brain regions of the DMN based on the generated activity indication signal of the DMN.

[0238] According to some exemplary embodiments, the system 302 repeats the receiving electrical signals, measuring EEG signals, determining activity of at least one network and delivery of a feedback signal to the subject, continuously, during at least one NF session, lasting between 1 minute and 3 hours, for example between 1 minute and 30 minutes, between 5 minutes and 30 minutes, between 10 minutes and 60 minutes, or any intermediate, smaller or larger time period.

[0239] According to some exemplary embodiments, the information collected about activity of the SN and / or activity of the DMN, optionally during the NF session, is used to generate an indication by the system 302 with information about at least one of, a suggested diagnosis of the subject, a suggested staging of a subject state, a suggested therapy for the subject, and / or a suggested efficacy of a treatment already delivered to the subject. In some embodiments, the system 302 processes the information collected about the activity of the SN and / or activity of the DMN, to classify the subject and / or the activity. In some embodiments, the system 302 generates the indication based on the classification, optionally based on comparison of the information collected from the subject to information collected from one or more different subjects or a population of subjects, optionally having similar characteristics to the subject or to the determined activity of at least one network of the SN and the DMN.

[0240] According to some exemplary embodiments, the system 302 is used for delivery of neurofeedback, for example as described in International Patent Application Publication number WO2023175610A1 and / or in International Patent Application Publication number WO2024038452A1, incorporated herein as a reference in their entirely.

[0241] Exemplary model (EFP)

[0242] Without being bound by and theory, relative to functional magnetic resonance imaging (fMRI), used for detecting BOLD activity of brain regions, or PET, electroencephalogram (EEG) constitutes a much more scalable recording device. However, EEG has low spatial resolution, and therefore has low efficiency in detecting activity originating from specific brain regions. To combine the advantages of the two measurement methods (fMRI and EEG), a tool was developed to predict fMRI BOLD activity in specific brain regions based on EEG alone. After simultaneous recording of EEG and fMRI signals, advanced data registration and statistical techniques were applied on the recorded signals to correlate data from the two modalities. The result was a prediction model, also termed EFP, that now allows conversion of an EEG signal into a surrogate indicator of the BOLD fMRI signal in specific brain regions. An amygdala-based EFP is described in International Patent Application Publication number WO2012104853A2, incorporated herein as a reference in its entirely. A VS-based EFP is described in International Patent Application Publication number WO2021260697 Al incorporated herein as a reference in its entirely.

[0243] According to some exemplary embodiments, the EFP model is a weight coefficient matrix, optionally of at least 10 coefficients, for EEG transformed to time delay x frequency obtained from statistical correlation between simultaneously acquired EEG and fMRI recordings with a volume of interest (VOI) covering a specific brain region. In some embodiments, the coefficient matrix corresponds to frequency bands, electrodes and one or more time windows.

[0244] For example, a VS-based EFP is used to select electrical signals, for example EEG electrical signals recorded from EEG electrodes located for example at positions C4, F7, F8, T7, T8, P8, TP9 and TP10. Additionally, the EFP is used to select EEG electrical signals in a frequency range between 0-40 Hz, and in a time delay window between 0 and 30 seconds. Alternatively, the VS-based EFP is used to select electrical signals recorded from EEG electrodes located for example at positions C4, C3, Cz, P3, P4, Pz, referenced to FCZ, filtered in a bandpass filter between 0.5-40 Hz, and in a time delay window that range between 3 and 13.1 seconds.

[0245] For example, an amyg-based EFP is used to select electrical signals recorded from EEG electrode located for example at position Pz referenced to FCZ, filtered in a bandpass filter between 0.5-60 Hz, and in a time delay window that ranges between 0 and 12 seconds

[0246] Exemplary detailed process for determining activity of a brain network

[0247] Reference is now made to fig. 4 depicting a detailed process for determining activity of at least one brain network, for example a SN and / or a DMN, according to some exemplary embodiments of the invention. In some embodiments, the process described in fig. 4 is performed by a system for determining activity of at least one brain network, for example the system 302 described in fig. 3.

[0248] According to some exemplary embodiments, information about a subject is optionally received, at block 402. In some embodiments, the information comprises information about a clinical and / or a mental state of the subject, optionally medical history of the subject, information about at least one symptom of a mental disorder from which the subject suffers, age, and / or gender. In some embodiments, the information is used for selecting a more suitable stimulus to be displayed to the subject and / or to adjust one or more parameters of an assessment session, for example duration of the assessment session, based on a provided information, optionally to personalize the assessment session for the specific subject. Optionally, a model for activity of the at least one network is selected based on the information provided at block 402. Optionally, locations for positioning electrodes on a subject body, for example on a subject head, are selected based on the information provided at block 402.

[0249] According to some exemplary embodiments, an assessment session is optionally initiated, at block 404. In some embodiments, an assessment session duration is between 1 minute and 60 minutes, for example between 1 minute and 30 minutes, between 5 minutes and 20 minutes, between 10 minutes and 40 minutes, or any intermediate, shorter or longer time period.

[0250] According to some exemplary embodiments, at least one stimulus, for example a sensory stimulus, is presented to the subject, at block 406. In some embodiments, the stimulus is presented to the subject using the user interface 310 shown in fig. 3. In some embodiments, the stimulus is a stimulus selected to affect activation of the at least one brain network, for example to affect activation of the SN and / or the DMN. In some embodiments, the same stimulus is used to affect both networks. Alternatively, at least two different stimuli are used, each to affect a different network of the SN and the DMN.

[0251] According to some exemplary embodiments, presenting of at least one stimulus comprises presenting a visual and / or audio interface to the subject, for example using the display 314 and / or the speaker 312 shown in fig. 3. Optionally, the interface, or stimulus is dynamic, for example changes over time, for example a dynamic sound and / or video. In some embodiments, the interface comprises the at least one stimulus for affecting activation of one or both of SN and DMN. Alternatively, the presented interface comprises at least one specific stimulus, one for each network of the SN and DMN.

[0252] According to some exemplary embodiments, signals, for example electric signals, are recorded from the subject brain, at block 408. In some embodiments, the signals are recorded by at least one electrode or a plurality of electrodes coupled to the subject head or ears. In some embodiments, the at least one electrode or the plurality of electrodes are in contact with the subject scalp. In some embodiments, the signals are recorded by at least one electrode, for example the at least one electrode 316 shown in fig. 3. In some embodiments, the signals are recorded during the presenting at block 406. According to some exemplary embodiments, EEG signals are measured at block 410. In some embodiments, the EEG signals are measured from the signals recorded at block 408. In some embodiments, the EEG signals are measured by the control circuitry 306.

[0253] According to some exemplary embodiments, the EEG signals are processed using at least one brain network activity model, for example an activity model of the SN activity comprising an amygdala-based EFP, or an activity model of the DMN activity comprising a VS -based EFP. In some embodiments, the processing is performed by a control circuitry, for example the control circuitry 306. In some embodiments, the same measured EEG signals are processed using the amygdala-based EFP, and the VS-based EFP for determining the activity of both of the SN and the DMN. Alternatively, only the amygdala-based EFP, or the VS-based EFP, are used.

[0254] According to some exemplary embodiments, processing the EEG signals using the at least one activity model comprises selecting EEG signals recorded by a selected set of electrodes, having a frequency range between 0-100 Hz, for example having frequency range between 0-20 Hz, 0-30 Hz, 0-40 Hz, 0-50 Hz, 0-60 Hz, 5 Hz to 60 Hz, 10 Hz- 80 Hz, 5Hz to 80 Hz, or any intermediate, smaller o larger frequency range, and a time delay window between 0 and 80 seconds, for example a time delay window between 0 and 20 seconds, 0 and 30 seconds, 0 and 40 seconds, 0 and 60 seconds, 10 and 50 seconds, or any intermediate, smaller or larger time window.

[0255] According to some exemplary embodiments, a results of the processing is the generation of an output signal indicating an activity of the at least one brain network during and / or following the presenting of the stimulus.

[0256] According to some exemplary embodiments, the activity of the network is determined at block 414. In some embodiments, the activity of the network is determined based on the results of the processing, for example based on the output signal which indicates activity of the brain network. In some embodiments, the activity of the network is determined by a control circuitry, for example the control circuitry 306.

[0257] According to some exemplary embodiments, the activity is optionally determined based on a determined relation between an activity of the network and a reference, for example a reference value, at block 416. In some embodiments, the reference comprises previously determined activity of the at least one brain network, baseline activity of the at least one network, a measure of central tendency, for example a mean, mode or median, of activity of the at least one brain network in a population of subjects. In some embodiments, determining a relation comprises determining a relation between the output signal generated in response to an activating sensory stimulus, indicating an active brain network and an output signal generated in response to a neutral sensory stimulus indicating baseline activity of the brain network.

[0258] According to some exemplary embodiments, determining an activity of a network, for example by the control circuitry comprises calculating or measuring a value, optionally a measure of central tendency of the value, indicating activity of the at least one network, optionally during a predetermined time period. In some embodiments, determining of activity of the at least one network comprises determining activity of at least two brain regions of the at least one brain network, optionally interacting with each other. In some embodiments, determining activity of the at least two brain regions comprises calculating or measuring at least one value indicating activity of the at least two brain regions, optionally simultaneous activity of both regions. In some embodiments, the at least one value is a measure of central tendency of the activity of the at least two brain regions, optionally a measure of central tendency of a simultaneous activity of the at least two brain region.

[0259] According to some exemplary embodiments, an indication with regard to the determined activity is generated at block 418. In some embodiments, the indication with regard to the determined activity comprises at least one of, an indication about activity of the at least one brain network during the session, for example in response to the sensory stimulus, a suggestion how to modify a treatment already provided to the subject in view of the determined activity, a suggestion for a treatment of the subject in view of the determined activity, an indication about an efficacy of a treatment already provided to the subject, a diagnosis of a subject with at least one mental disorder, and / or an indication regarding a state or a stage of at least one mental disorder from which the subject suffers.

[0260] According to some exemplary embodiments, the indication is delivered, at block 420. In some embodiments, the indication is delivered, optionally as a human detectable indication, to the subject or to a supervisor or an expert, for example a physician, or a healthcare professional.

[0261] Exemplary diagnosis

[0262] According to some exemplary embodiments, the determining of activity of one or both of the SN and / or the DMN is used by an expert, for example a physician or a healthcare professional, to diagnose a subject with a mental disorder or with an emotional disorder. In some embodiments, the diagnosis is performed by determining a relation between activity of one or both of the SN and / or the DMN in the subject, and a reference indicating activity of one or both of the brain networks in a group of subjects diagnosed with a specific mental disorder or with a specific emotional disorder. Optionally, a diagnosis decision making process takes into consideration the activity of one or both networks, and clinical, medical and / or personal information about the subject.

[0263] According to some exemplary embodiments, the SN and / or the DMN models, for example EFPs are used to measure the reactivity of a network to a relevant stimulus, thereby predicting the efficacy of treatment - and the best choice of treatment before the treatment begins.

[0264] For example, in some embodiments, SN reactivity to an emotional stimuli is measured using the EFP, for example the amyg-based EFP. In some embodiments, if the SN is hyperactive (for example as in PTSD, anxiety disorders, etc.), then treatment types that result in downregulation of SN are expected to help, such as CBT or NF.

[0265] For example, in some embodiments, VS reactivity to reward stimuli is measured using the EFP, for example a VS-based EFP. In some embodiments, if VS is hypoactive (as in anhedonia) then treatment types that result in up-regulation of reward response are expected to help, such as VS-EFP NF, for example as described in International Patent Application Publication Number WO2023175610A1, titled “Depression treatment”, incorporated herein as a reference in its entirety.

[0266] Reference is now made to fig. 5A, depicting actions performed by an expert when diagnosing a subject with a mental and / or an emotional disorder, according to some exemplary embodiments of the invention.

[0267] According to some exemplary embodiments, information about the subject is collected at block 502. In some embodiments, the information comprises at least one of, clinical information, information about at least one symptom from which the subject suffers, medical history, family medical history, gender, age, occupation, information about at least one treatment provided to the subject, and / or information about at least one event in a life of the subject that can be associated with developing a mental and / or an emotional disorder. In some embodiments, an expert collects the information using at least one questionnaire filled by the subject or together with the subject, medical records, and / or based on one or more discussions with the subject. Alternatively or additionally, the expert collects the information from observing subject behavior and / or from public digital resources, for example social media.

[0268] According to some exemplary embodiments, the expert receives an indication about an activity of one or both of the SN and the DMN at block 504. In some embodiments, the input is generated by a system that determines activity of the one or both the SN and the DMN, for example as described in fig. 4.

[0269] According to some exemplary embodiments, optionally a state of the subject is determined, at block 506. In some embodiments, the state is determined based on the activity determined at block 506. In some embodiments, determining a state of the subject comprises determining a mental and / or an emotional state of the subject.

[0270] According to some exemplary embodiments, the state of the subject is determined based on information received from a system that processes data about the subject and / or the determined activity of the SN and / or the DMN, and generates an output with at least one indication about the subject state.

[0271] According to some exemplary embodiments, the expert diagnoses the subject with a mental disorder and / or an emotional disorder, at block 508. In some embodiments, the expert diagnoses the subject based on the activity of the SN and / or DMN, or portions thereof, for example at least two brain regions of the SN and / or at least two brain regions of the DMN. Additionally or optionally, the expert diagnoses the subject based on the information collected about the subject at block 502 and / or the subject state optionally determined at block 506.

[0272] According to some exemplary embodiments, the expert optionally determines a suitability of the subject for at least one treatment, at block 51O.In some embodiments, the expert determines the subject suitability for the treatment based on the diagnosis, and optionally based on the subject state. In some embodiments, the expert determines subject suitability for the treatment based on a current activity of the SN and / or DMN, and the ability of the treatment to change the activity in a desired direction and in a desired extent during a predetermined time period.

[0273] According to some exemplary embodiments, the expert selects a treatment, at block 512. In some embodiments, the treatment is selected based on at least one of, a diagnosis of the subject, a state of the subject, the activity of one or both of the SN and the DMN, the suitability of the subject to the treatment and / or the ability of the selected treatment to improve the subject state and / or at least one symptom of the disorder. In some embodiments, the treatment comprises at least one of, a drug treatment, a psychotherapy, a behavioral therapy, a biofeedback, a neurofeedback, and / or a CBT.

[0274] For example, in anxiety disorders, for example panic disorder, social anxiety disorder, generalized anxiety disorder, specific phobia, agoraphobia, health anxiety, obsessive-compulsive disorder, PTSD, separation anxiety disorder, and selective mutism, the SN is hyperactive and the subject optionally suffers from emotion dysregulation (elevated emotional reactivity), the treatment comprises at least one of, Cognitive Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Exposure Therapy, Acceptance and Commitment Therapy (ACT), Selective Serotonin Reuptake Inhibitors (SSRIs), Serotonin-Norepinephrine Reuptake Inhibitors (SNRIs), Benzodiazepines, Beta-Blockers, and / or Tricyclic Antidepressants (TCAs). In some embodiments, disorders associated with abnormal DMN Activity comprise, major depressive disorder (MDD), anxiety disorders, PTSD, schizophrenia, bipolar disorder, attention-deficit / hyperactivity disorder (ADHD), autism spectrum disorder (ASD), Alzheimer’s disease, mild cognitive impairment (MCI), obsessive-compulsive disorder (OCD), borderline personality disorder (BPD), substance use disorders (SUD), and dissociative disorders.

[0275] In some embodiments, a treatment for MDD comprises at least one of, pharmacotherapy with SSRIs, SNRIs, or atypical antidepressants such as bupropion, psychotherapies like Cognitive Behavioral Therapy (CBT), Mindfulness-Based Cognitive Therapy (MBCT), neuromodulatory interventions like repetitive transcranial magnetic stimulation (rTMS), ketamine infusions and / or psychedelic-assisted therapy (e.g., psilocybin).

[0276] In some embodiments, a treatment for anxiety disorders comprises at least one of, SSRIs, SNRIs, benzodiazepines, CBT, Acceptance and Commitment Therapy (ACT), MBCT, mindfulness practices, neurofeedback, and / or lifestyle interventions for example aerobic exercise and breathing techniques.

[0277] In some embodiments, a treatment for PTSD comprises at least one of, psychotherapies such as trauma-focused CBT, EMDR, Prolonged Exposure Therapy, Narrative Exposure Therapy, pharmacological treatments for example SSRIs and prazosin, neuromodulation via rTMS or tDCS, and / or MDMA-assisted psychotherapy.

[0278] In some embodiments, a treatment for schizophrenia comprises at least one of, atypical antipsychotics like risperidone and olanzapine, Cognitive Behavioral Therapy for psychosis (CBTp), cognitive remediation therapy, and / or neuromodulatory methods for example tDCS or rTMS.

[0279] In some embodiments, a treatment for bipolar disorder comprises at least one of, mood stabilizers (e.g., lithium, valproate) and atypical antipsychotics, psychoeducation, CBT, Interpersonal and Social Rhythm Therapy (IPSRT), rTMS and / or mindfulness.

[0280] In some embodiments, a treatment for ADHD comprises at least one of, stimulant medications such as methylphenidate and amphetamines, non- stimulants like atomoxetine, behavioral therapy, executive function coaching, cognitive training, and / or neurofeedback.

[0281] In some embodiments, a treatment for ASD comprises at least one of, behavioral therapies like Applied Behavior Analysis (ABA), social skills training, speech therapy, occupational therapy, pharmacological interventions such as risperidone, mindfulness, neurofeedback, and / or non-invasive brain stimulation. In some embodiments, a treatment for AD and MCI comprises at least one of, cholinesterase inhibitors, memantine cognitive stimulation, memory training, and / or lifestyle interventions like aerobic exercise.

[0282] In some embodiments, a treatment for OCD comprises at least one of, SSRIs, clomipramine, mindfulness, neuromodulation like Deep Brain Stimulation (DBS) and / or rTMS.

[0283] In some embodiments, a treatment for BPD comprises at least one of, psychotherapy Dialectical Behavior Therapy (DBT), Mentalization-Based Therapy (MBT), Schema Therapy, pharmacotherapy, mindfulness, emotion regulation strategies.

[0284] In some embodiments, a treatment for SUD comprises at least one of, Motivational Interviewing, CBT, Contingency Management, group-based interventions, pharmacological support depends on the substance (e.g., naltrexone for alcohol, buprenorphine for opioids), Mindfulness-Based Relapse Prevention (MBRP), neurofeedback and / or TMS.

[0285] In some embodiments, a treatment for dissociative disorders comprises at least one of, phase-oriented trauma therapy, psychodynamic therapy, EMDR, and / or mindfulness.

[0286] Reference is now made to fig. 5B, depicting actions performed by a system for diagnosing a subject based on activity of the SN and / or DMN, according to some exemplary embodiments of the invention.

[0287] According to some exemplary embodiments, a system, for example system 302 described in fig. 3 or any part of the system, for example control unit 304, receives information about a subject, at block 520. In some embodiments, the information comprises at least one of, clinical information, information about at least one symptom from which the subject suffers, optionally medical history, family medical history, gender, age, occupation, information about at least one treatment provided to the subject, and / or information about at least one event in a life of the subject that can be associated with developing a mental and / or an emotional disorder. In some embodiments, the information is inserted as an input signal to the system using a user interface configured to receive input data, for example user interface 310, and / or a user interface that is in communication with the system or is part of the system, for example a user interface of the remote device 330 or a user interface of the supervisor device 328.

[0288] According to some exemplary embodiments, the system determines activity of the SN and / or the DMN, at block 522. In some embodiments, the system determines activity of the SN and / or the DMN, as described in fig. 4.

[0289] According to some exemplary embodiments, the system generates a diagnosis indication, at block 524. In some embodiments, the system generates the diagnosis information based on the activity determined at block 522, and optionally based on the information received at block 520. In some embodiments, the system generates the diagnosis information using at least one algorithm, formula or a lookup table, stored in a memory of the system, associating at last one of, a mental state, a cognitive state, an emotional state, a mental disorder, a disorder according to Diagnostic and Statistical Manual of Mental Disorders (DSM) or symptoms of disorders e.g emotion dysregulation, with the activity determined at block 522 and optionally with the information received at block 520.

[0290] According to some exemplary embodiments, the system delivers a diagnosis indication, at block 526. In some embodiments, the indication comprises a diagnosis indication generated at block 526. In some embodiments, the delivered indication comprises a human detectable indication, for example a visual and / or an audio indication that can be detected by a human. In some embodiments, the system delivers the indication at block 526 using at least one user interface of the system, for example user interface 310 or a user interface of the remote device 330 and / or a user interface of the supervisor device 328.

[0291] According to some exemplary embodiments, the system optionally selects at least one treatment, at block 528. In some embodiments, the system selects the at least one treatment using at least one algorithm, formula or a lookup table, stored in a memory of the system, associating at last one treatment with the diagnosis indication generated at block 524. In some embodiments, the at least one treatment comprises a drug treatment, a psychotherapy treatment, and / or a CBT.

[0292] According to some exemplary embodiments, the system optionally delivers an indication with the selected treatment, at block 530. In some embodiments, the delivered indication comprises a human detectable indication, for example a visual and / or an audio indication that can be detected by a human. In some embodiments, the system delivers the indication at block 530 using at least one user interface of the system, for example user interface 310 or a user interface of the remote device 330 and / or a user interface of the supervisor device 328.

[0293] According to some exemplary embodiments, the indication with the selected treatment comprises a suggestion to apply the selected treatment.

[0294] Exemplary determining efficacy of a treatment

[0295] According to some exemplary embodiments, the determining of activity of at least one brain network, comprising the SN and / or the DMN, is used for determining an efficacy of a treatment provided to the subject. In some embodiments, the treatment efficacy is determined based on the effect the treatment has on the activity of the at least one network. In some embodiments, for example, if the treatment is expected to down regulate the activity of the SN, but the activity of the SN remains unchanged, the treatment has low efficacy. In some embodiments, for example, if the treatment is expected to reduce activity of the DMN, but the activity of the DMN remains unchanged, the treatment has low efficacy.

[0296] According to some exemplary embodiments, the activity of the SN and / or the DMN networks is determined while the subject receives the treatment, for example during a treatment course, at one or more time points. In some embodiments, the efficacy of the treatment is determined during the treatment course. In some embodiments, determining an efficacy of a treatment comprises, assessing treatment efficacy, estimating treatment efficacy or calculating a treatment efficacy value indicating treatment efficacy.

[0297] A potential advantage of determining efficacy of a treatment for a disorder based on a determined activity of the SN and / or the DMN, maybe that it allows to detect an effect of a treatment on a brain of a subject in a short time period, and prior to appearance of visible changes in at least one symptom of the disorder, for example prior to changes in a behavior of the subject, in response to the treatment.

[0298] Reference is now made to a process for determining efficacy of a treatment performed by an expert, according to some exemplary embodiments of the invention.

[0299] According to some exemplary embodiments, a subject is diagnosed by an expert, for example a physician or any other healthcare professional, with a disorder, at block 602. In some embodiments, the subject is diagnosed with a mental or an emotional disorder, for example with at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder and / or Alzheimer’s disease (AD).

[0300] According to some exemplary embodiments, a treatment is provided by the expert to the diagnosed subject, at block 604. In some embodiments, the treatment comprises a drug treatment. Alternatively or additionally, the treatment comprises at least one of, psychotherapy and / or CBT.

[0301] According to some exemplary embodiments, the expert receives an indication about activity of the SN and / or the DMN in the subject, at block 606. In some embodiments, the indication is about the activity of the SN and / or the DMN in the subject following the treatment. In some embodiments, the activity is determined by a system, at least about 5 minutes, at least about 1 hour, at least about 3 hours, at least about 6 hours, at least about 12 hours, at least about 24 hours, at least about 2 days, at least about 5 days, at least about 10 days, or any intermediate, shorter or longer time period following the providing of the treatment, for example a drug treatment, to the subject. According to some exemplary embodiments, the expert determines efficacy of the provided treatment, at block 608. In some embodiments, the expert determines treatment efficacy based on the received input about the activity of the SN and / or the activity of the DMN.

[0302] Optionally, the expert monitors periodically the activity of the SN and / or the activity of the DMN, during a predetermined time period of between about 30 minutes from initiating the treatment, and up to about 1 week from initiating the treatment, to determine the efficacy of the treatment. Optionally, the expert monitors periodically the activity of the SN and / or the activity of the DMN, during a period in which the treatment is provided to the subject, for example to make sure the treatment remains efficacious during that period.

[0303] According to some exemplary embodiments, the treatment is optionally modified, at block 612. In some embodiments, the treatment is modified if the efficacy of the treatment is lower than a desired, for example a target, efficacy. In some embodiments, modifying a treatment comprises stopping the treatment or modifying at least one parameter of the treatment, for example a dosage and / or administration timing of a drug, a length of each psychotherapy or CBT session, and / or frequency of psychotherapy and / or CBT sessions. In some embodiments, modifying treatment comprises replacing the treatment with at least one different treatment. In some embodiments, modifying treatment comprising combining the treatment provided at block 604 with at least one additional treatment.

[0304] Reference is now made to fig. 6B, depicting actions performed by a system for determining the efficacy of a treatment provided to a subject, according to some exemplary embodiments of the invention.

[0305] According to some exemplary embodiments, a system, for example system 302 shown in fig. 3, receives information a subject, at block 620. In some embodiments, the information is received as described at block 520 in fig. 5B.

[0306] According to some exemplary embodiments, the system receives information about a treatment provided to the subject, at block 622. In some embodiments, the information comprises administration regime of at least one drug, dosage of the at least one drug, length of a psychotherapy or a CBT session, and / or frequencies of psychotherapy or CBT sessions.

[0307] According to some exemplary embodiments, the system determines activity of the SN and / or the DMN, at block 624. In some embodiments, the system determines activity of the SN and / or the DMN, as described in fig. 4.

[0308] According to some exemplary embodiments, the system determines efficacy of the treatment provided to the subject, at block 626. In some embodiments, the system determines the efficacy of the treatment using at least one algorithm, a formula and / or a lookup table, stored in a memory of the system, for example the memory 308 of the control unit 304. In some embodiments, the system determines the efficacy of the treatment based on a change in activity of the SN and / or the DMN in a desired direction following initiation of the treatment, optionally compared to the activity of the SN and / or the DMN prior to the treatment.

[0309] According to some exemplary embodiments, the system delivers an indication about the determined efficacy of the treatment, at block 628. In some embodiments, the indication is a human detectable indication. In some embodiments, the indication is delivered using a user interface of the system, for example the user interface 310 shown in fig. 3, and / or a user interface of the remote device 330 and / or a user interface of the supervisor device 328.

[0310] According to some exemplary embodiments, the system optionally delivers an indication to modify the existing treatment, at block 630. In some embodiments, the indication comprises suggestions to stop the existing treatment, to modify at least one parameter of the existing treatment, for example a timing or a dosage of the treatment, to replace the existing treatment with at least one different treatment or to combine the existing treatment with at least one additional treatment.

[0311] Exemplary determining activity of at least two networks

[0312] According to some exemplary embodiments, activity of at least two brain networks, for example activity of the SN and activity of the DMN are determined using the same set of signals recorded from a brain of a subject.

[0313] Reference is now made to fig. 7A, depicting a process for determining activity of at least two brain networks, according to some exemplary embodiments of the invention.

[0314] According to some exemplary embodiments, during a session, for example an assessment session, for determining activity of at least one brain network, at least one stimulus is delivered to the subject, at block 702. In some embodiments, the at least one stimulus is designed to affect activation of the SN and the DMN. In some embodiments, affecting activation of the SN comprises affecting activation of at least two nodes of the SN , for example at least two nodes from the list of nodes including, the rPFC, the dACC, the SMG, the Amyg, and the alns, for example as shown in fig. 2A. In some embodiments, affecting activation of the DMN comprises affecting activation of at least two nodes of the DMN , for example at least two nodes from the list of nodes including, the MFG, the mPFC, the VS, the MTG, the HC, the PCC, and the IPL, for example as shown in fig. 2B.

[0315] According to some exemplary embodiments, affecting activation of at least two nodes comprises modifying activation of the at least two nodes simultaneously, sequentially, or with a time difference between the at least two nodes which is shorter than 30 seconds, for example shorter than 20 seconds, shorter than 10 seconds, shorter than 5 seconds, shorter than 1 second, shorter than 0.5 second, or any intermediate, shorter or longer time period. In some embodiments, affecting activation of at least two nodes comprises, increasing activation of at least one node or both of the nodes, decreasing activation of at least one node or both of the nodes, or increasing activation of at least one node and decreasing activation of at least one different node of the at least two nodes.

[0316] According to some exemplary embodiments, the at least one stimulus comprises a sensory stimulus, optionally delivered to the subject as an audio and / or visual indication. Alternatively or additionally, the at least one stimulus is optionally delivered to the subject as a tactile indication.

[0317] According to some exemplary embodiments, the at least one stimulus comprises at least two different stimuli, each is designed to affect activation of a different network of the SN and the DMN networks. In some embodiments, the at least two different stimuli are delivered to the subject using the same user interface or using different user interfaces. In some embodiments, the at least two different stimuli are delivered to the subject simultaneously or with a time difference shorter than 5 minutes, for example with a time difference shorter than 3 minutes, shorter than 2 minutes, shorter than 1 minute, shorter than 30 seconds, shorter than 10 seconds, shorter than 1 second, shorter than 0.5 second, or any intermediate, shorter or longer time difference.

[0318] According to some exemplary embodiments, the at least two stimuli are delivered to the subject during the same time period, or optionally each is delivered during a different time period.

[0319] According to some exemplary embodiments, signals are recorded from the subject brain, at block 704. In some embodiments, the signals are recorded by at least one electrode coupled to a head of the subject, for example at least one electrode 316 shown in fig. 3. In some embodiments, the signals are recorded during the delivery of the at least one stimulus at block 702.

[0320] According to some exemplary embodiments, EEG signals are measured from the recorded signals, at block 706. In some embodiments, at least one set of EEG signals is measured from the recorded signals.

[0321] According to some exemplary embodiments, the at least one set of EEG signals is process with at least one activity model of the SN to generate an indication of activity of the SN, and at least one activity model of the DMN to generate an indication of activity of the SN. In some embodiments, the same set of measured EEG signals is processed using the at least one activity model of the SN and the at least one activity model of the DMN, to generate activity indications of both the SN and the DMN.

[0322] According to some exemplary embodiments, the generated indication for each network, the SN and the DMN, indicates activity of the network or changes thereof, during the delivery of the at least one stimulus to the subject at block 702.

[0323] According to some exemplary embodiments, activity of the SN and the DMN is determined, at block 710. In some embodiments, the activity is determined based on the activity indications generated at block 708.

[0324] According to some exemplary embodiments, a relation between the determined activity of the SN and the DMN is optionally determined at block 712. In some embodiments, the relation comprises a ratio or a difference between activity level of both networks, and / or a relation or difference between timing of activity of both networks, for example in case there is a time difference between a change in activity of the networks.

[0325] According to some exemplary embodiments, a relation between the determined activity of the SN and / or the DMN and at least one reference is optionally determined at block 714. In some embodiments, the at least one reference comprises a previously measured activity of one or both of the SN and the DMN, in the subject or in a population.

[0326] According to some exemplary embodiments, at least one indication is generated and delivered by the system, at block 716. In some embodiments, the indication is a human detectable indication. In some embodiments, the indication is delivered to the subject from which the signals are recorded at block 704, or to an expert, for example a physician or a supervisor of the assessment session. Optionally, the indication is delivered using the user interface 310 shown in fig. 3, and / or at least one different user interface of the system 302, for example a user interface of the remote device 330 or a user interface of the supervisor device 332. In some embodiments, the delivered indication includes information about at least one of, activity of one or both of the SN and the DMN networks, a determined relation between the activity of the two networks as optionally determined at block 712, and / or a determined relation between the activity of one or both of the networks and the at least one reference, as optionally determined at block 714.

[0327] Exemplary SN and / or DMN neurofeedback

[0328] According to some exemplary embodiments, NF training is provided to the subject, to train the subject to self-modulate activity of the SN and / or DMN networks. In some embodiments, during the NF training the subject is trained to apply a mental strategy, for example a thought, a mental and / or a cognitive exercise that is suitable for modulating the activity of at least one network of the SN and the DMN networks, in a desired direction, for example to upregulate or downregulate activity of the at least one network. Optionally, in case the NF treatment is directed to affect both networks, the subject is trained to apply two different mental strategies, one for modulating activity of the SN in a target direction and one for modulating activity of the DMN in a similar or a different target direction. Alternatively, the subject is trained to apply a single mental strategy to modulate both networks in a similar direction or each network in a different direction.

[0329] As used herein, the meaning of modulating a network in a direction, may be upregulating or downregulating activity of a network, or at least one parameter related to the activity of a network, for example activity duration, activity intensity, strengthening connectivity between network regions or weakening connectivity between network regions.

[0330] As used herein, the meaning of the term “target direction” in the context of network modulation may be modulating a network activity in a desired direction, for example in a direction that generates a positive clinical outcome, for example in a direction that reduces one or more symptoms of a disorder, for example a mental, behavioral, and / or cognitive disorder.

[0331] According to some exemplary embodiments, a NF training of the SN is provided to a subject, for example a patient diagnosed with a mental and / or an emotional disorder, optionally to train the subject to regulate a response of the subject to emotional stimuli. In some embodiments, the subject is trained to regulate an emotional reactivity of the subject. In some embodiments, the subject is trained to upregulate their emotional reactivity and / or their response to an emotional stimuli. Alternatively, the subject is trained to downregulate their emotional reactivity and / or their response to an emotional stimuli.

[0332] According to some exemplary embodiments, the NF training trains a subject to affect, for example to downregulate SN activity. In some embodiments, downregulation of SN activity causes reduction in visibility or sound of an interface, for example a stimulus presented to the subject. In some embodiments, training a subject to downregulate SN activity allows, for example to treat hyper-reactivity of the salience network characterizing certain disorders such as PTSD and anxiety. Optionally, the NF training teaches the subject to emotionally disengage from negative stimuli.

[0333] According to some exemplary embodiments, the NF training trains a subject to upregulate SN activity. In some embodiments, upregulation of SN activity causes an increase in visibility or sound of an interface, for example a stimulus presented to the subject. In some embodiments, training a subject to upregulate SN activity allows, for example to treat hypo-reactivity of the salience network characterizing certain disorders such as bipolar disorder. Optionally, the NF training teaches the subject to emotionally engage with positive stimuli.

[0334] According to some exemplary embodiments, the NF training of the SN is used to treat chronic pain, for example patients diagnosed with fibromyalgia. In some embodiments, the NF is used to train the subject to downregulate SN reactivity when encountering a negative stimuli, while optionally relating to a body response. For example, a NF trainee observes a body of a human avatar from a third-person perspective, and body movement. Optionally, the avatar body language reflects physical discomfort (such as fidgeting, itching, neurotic body language etc.). In some embodiments, during the NF training the trainee is instructed to calm down the avatar. In some embodiments, when the trainee succeeds in down regulating the SN, for example by down regulating an amyg-based EFP, the avatar’s body language becomes more relaxed and comfortable. In some embodiments, the avatar body language and behavior is personalized according to a physiological discomfort of the trainee.

[0335] According to some exemplary embodiments, the NF is used to target salience-based processes, including the salience network reactivity to negative emotional stimuli, positive emotional stimuli, and / or to negative interoceptive sensation. In some embodiments, the NF is a process-targeted combination of an interface and an EFP, modified to address specific mental states of SN dysregulation. For example, in some embodiments, in order to treat anger disorders, a subject, for example a trainee, is trained to downregulate SN activity in response to stimuli that evoke anger or annoyance. In some embodiments, to treat anxiety disorders the subject is trained to downregulate activity of the SN in response to stimuli that evoke anxiety. In some embodiments, to treat depression, the subject is trained to up-regulate SN activity in response to stimuli that evoke joy.

[0336] According to some exemplary embodiments, a NF training for affecting activity of the SN can be used to treat at least one disorder presenting with symptoms of emotion and / or sensory dysregulation, for example, post-traumatic stress disorder (PTSD), chronic pain disorders such as Fibromyalgia, attention-deficit / hyperactivity disorder (ADHD), major depressive disorder (MDD) and other mood disorders, schizophrenia and psychosis, substance use disorders (SUD), and / or anxiety disorders

[0337] According to some exemplary embodiments, the NF training trains a subject to affect, for example to upregulate DMN activity. According to some exemplary embodiments, a NF training for affecting activity of the DMN, can be used to treat at least one of, major depressive disorder (MDD), bi-polar disorder and other mood disorders, attention-deficit / hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), schizophrenia and psychosis, and substance use disorders (SUD).

[0338] According to some exemplary embodiments, the NF training is used to train the subject to modulate activity of the SN, while measuring activity of the SN and providing a feedback signal to the subject, based on measured activity of the SN or changes thereof. Alternatively, the NF training is used to train the subject to modulate activity of the SN, while measuring activity of the SN and of the DMN, and providing a feedback signal to the subject, based on measured activity of the SN or changes thereof. Optionally, measurements of the DMN activity may be used as a reference for the measured activity of the SN, and / or for determining a state, for example a clinical state of the subject during the NF training.

[0339] According to some exemplary embodiments, the NF training is used to train the subject to modulate activity of the DMN, while measuring activity of the DMN and providing a feedback signal to the subject, based on measured activity of the DMN or changes thereof. Alternatively, the NF training is used to train the subject to modulate activity of the DMN, while measuring activity of the SN and of the DMN, and providing a feedback signal to the subject, based on measured activity of the DMN or changes thereof. Optionally, measurements of the SN activity may be used as a reference for the measured activity of the DMN, and / or for determining a state, for example a clinical state of the subject during the NF training.

[0340] According to some exemplary embodiments, the NF training is used to train the subject to modulate activity of both the DMN and of the SN, while measuring activity of both the DMN and SN and providing a feedback signal to the subject, optionally based on a score calculated according to a relation between the activity of both networks.

[0341] Reference is now made to fig. 7B, depicting a process of a NF treatment for training a subject to modify activity of the SN and / or DMN, according to some exemplary embodiments of the invention.

[0342] According to some exemplary embodiments, a NF training, for example a NF treatment, comprises at least one NF session or a plurality of NF sessions. In some embodiments, an interval between two consecutive NF sessions is between 1 hour and 2 weeks, for example between 1 hour and 24 hours, between 12 hours and 72 hours, between 1 day and 1 week, or any intermediate, shorter or longer interval length.

[0343] According to some exemplary embodiments, a NF session is initiated at block 722.

[0344] According to some exemplary embodiments, a neutral indication is optionally delivered to a subject participating in the NF training, for example a trainee, at block 724. In some embodiments, the neutral indication comprises an audio and / or visual indication selected not to have an effect on the SN and / or on a DMN of the subject.

[0345] According to some exemplary embodiments, a baseline activity of the SN and / or of the DMN is optionally determined at block 726. In some embodiments, the baseline activity of the SN and / or of the DMN is determined in response to the neutral indication delivered to the subject at block 724. In some embodiments, the baseline activity of the SN and / or DMN is determined, for example as described at blocks 408, 410, 412 and 414 of fig. 4.

[0346] According to some exemplary embodiments, at least one stimulus, for example a sensory stimulus, is delivered to the subject, at block 728. In some embodiments, a stimulus is a sensory interface, for example an audio and / or a visual interface. In some embodiments, the stimulus is selected to affect activity of the SN and / or of the DMN. In some embodiments, the stimulus is delivered as a human detectable indication, for example as a visual and / or an audio indication. In some embodiments, for example as described above, the stimulus comprises a third person view of a scenario or an avatar.

[0347] According to some exemplary embodiments, the subject is instructed to modify the stimulus, at block 730. In some embodiments, the subject is instructed to modify the stimulus to reach a target stimulus, which is optionally a modified version of the stimulus that has particular sensory information, for example particular appearance or sound.

[0348] According to some exemplary embodiments, the activity of the SN and / or of the DMN is determined at block 732. In some embodiments, the activity of the SN and / or DMN is determined in response to the stimulus delivered at block 728 and following the instructions delivered to the subject at block 730. In some embodiments, the activity of the SN and / or DMN is determined, for example as described at blocks 408, 410, 412 and 414 of fig. 4.

[0349] According to some exemplary embodiments, in case the NF treatment comprises determining activity of both the SN and the DMN, the activity of each network is determined independently, using a different activity model, and optionally based on different epochs of EEG signals, for example epochs in which a stimulus specific to the network is delivered to the subject or following the delivery of the specific stimulus. In some embodiments, determining activity of both the SN and the DMN also includes determining a relation between the activity of the two networks, and optionally calculating a score according to the determined relation.

[0350] According to some exemplary embodiments, the stimulus is modified according to the determined SN activity and / or according to the determined DMN activity, at block 734. Optionally, the stimulus is modified based on the calculated score indicating a relation between the determined activity of the two networks. In some embodiments, for example as described above, if the NF is configured to train a subject to downregulate activity of the SN, a visibility and / or sound of the stimulus is decreased according to a reduction in the activity of the SN. In some embodiments, if the NF is configured to train a subject to upregulate SN activity, a visibility and / or sound of the stimulus is increased according to an increase in SN activity. In some embodiments, for example as described above, if the NF is configured to upregulate activity of the DMN, for example to increase reward circuitry activity, the stimulus comprises at least one reward anticipation task modified to increase reward anticipation, and / or music or sound associated with pleasure modified to increase pleasure of the subject when the DMN s modified in a target direction. Optionally, when the NF treatment is aimed to train the subject to modulate both the SN and the DMN, the at least one stimulus is modified in a way reflecting the change in activity of both systems, as determined at block 732. Optionally, in case a relation between activity of the DMN and the SN, is determined at block 732, the stimulus is modified at block 734 based on the determined relation.

[0351] According to some exemplary embodiments, the modified stimulus is delivered to the subject at block 736.

[0352] According to some exemplary embodiments, blocks 732,734 and 736 are repeated during the NF session, continuously.

[0353] According to some exemplary embodiments, a duration of the NF session, for example a time period in which a stimulus is continuously modified according to SN activity, is between 1 minutes and 120 minutes, for example between 5 minutes and 30 minutes, between 5 minutes and 20 minutes, or any intermediate, shorter or longer time duration.

[0354] According to some exemplary embodiments, the modified stimulus is delivered to the subject as a feedback signal for indicating a change in activity of the SN and / or DMN. Alternatively, a feedback signal which is different from the modified stimulus is delivered to the subject. In some embodiments, the modified stimulus or the feedback signal, indicates activity of both the SN and the DMN.

[0355] According to some exemplary embodiments, at least one feedback signal is used for indicating activity, for example simultaneous activity, of two or more networks. Optionally each dimension, or parameter, of the feedback signal indicates activity of a different network.

[0356] Reference is made to fig. 7C, depicting an exemplary single feedback signal indicating activity of both the SN and the DMN, according to some exemplary embodiments of the invention.

[0357] According to some exemplary embodiments, at least one feedback signal 740, for example a sound or an audio signal is used to indicate both activity of the SN or changes thereof, and activity of the DMN or changes thereof. In some embodiments, the amplitude of the feedback signal indicates the activity of the SN and the frequency of the feedback signal indicates the activity of the DMN. In some embodiments, for example as shown in fig. 7C, a relation between the activity of the SN and the DMN changes during a NF session, and changes in the sound amplitude and sound frequency indicate these changes in activity.

[0358] According to some exemplary embodiments, the neurofeedback is delivered, for example as described in International Patent Application Publication number WO2023175610A1 and / or in International Patent Application Publication number WO2024038452A1, incorporated herein as a reference in their entirely.

[0359] Exemplary study

[0360] Reference is now made to fig. 8 depicting a schematic illustration of a methodological approach of a study performed by the applicant. The figure includes the general study design and analysis approach.

[0361] Two groups of 35 healthy subjects each, a test group and a replication group, participated in the study. During the study, a subject 802 was placed inside a fMRI device 804, with EEG electrodes 806 coupled to its head. Concurrent EEG 810 and fMRI was acquired while the subject was presented with naturalistic audiovisual stimulation 808, for example emotional movies, clips and music. The fMRI signals 816 were preprocessed in 818 to yield a voxel-wise BOLD time-course 820. The EEG signals 810 were processed and weighted with an existing EFP model coefficients, also termed herein as an activity model or an EFP, to yield an EFP timecourse 814, which serves as an indication for activity. The two existing EFP models used in the study were an amygdala-based EFP model as described in International Patent Application Publication Number WO2012104853A2, titled “method and system for use in monitoring neural activity in a subject's brain”, and in Meir-Hasson et al., 2013 titled “an EEG Finger-Print of fMRI deep regional activation”, both incorporated herein as a reference in their entirety, and a VS- based EFP model as described in International Patent Application Publication Number WO2021260697A1, titled “ventral striatum activity”, and in Singer et al., 2023 titled “development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation”, both incorporated herein as a reference in their entirety.

[0362] To examine the relationship between the BOLD time course 820 and the EFP time course 814, a generalized linear model (GLM) analysis was performed using linear regression 822 over the entire task time-course, as well as a sliding-window correlation analysis 824. Reference is now made to figs. 9A and 9B depicting BOLD activation associated with the amygdala-based EFP.

[0363] Figs. 9A and 9B show GLM results of linear regression of voxel-wise BOLD to amyg- based EFP time-course in each of the tasks, displaying above-chance voxels (P<0.05, FDR corrected) (9A) within the right amygdala (dashed outline 902) and (9B) across the cortex, displayed inflated, where voxels significantly correlated with the amyg-based EFP are colored in orange and annotated 920, and voxels not significantly correlated with the amyg-based EFP are uncolored 922 . Replication data denoted ‘rep.’, amygdala defined by Automated anatomical labelling atlas 3 (AAL3 atlas, Rolls et al., 2020).

[0364] The results of fig. 9A show that the amyg-based EFP signal is correlated with voxel-wise bold signal located in a sub-region 904, 906, 908 and 910 of the amygdala region 902, for example in the right amygdala.

[0365] The results of fig. 9B shows that the amyg-based EFP signal is correlated with voxel-wise bold signal 920 located across the cortex.

[0366] Reference is now made to figs. 10, 11A, 11B, 12A and 12B depicting Large-scale cortical activity associated with amyg-EFP compared with VS -EFP. These figures show GLM results of linear regression of voxel- wise BOLD to EFP time-course for the amyg-based EFP and VS -EFP (also termed reward-system EFP) during movie scenes.

[0367] Fig. 10 shows above-chance voxels (P<0.05, FDR corrected) for amyg-based EFP (orange) and VS-EFP (blue) overlayed on the inflated cortex. This figure shows distribution of voxel-wise BOLD signal correlating with amyg-based EFP (orange) compared with distribution of voxel-wise BOLD signal correlating with VS-based EFP (blue), also referred herein as rewardsystem EFP or DMN EFP. The distribution of both type of signals shows separate cortical distribution patterns, indicating that each of the EFP models targets and / or can be used as an activity biomarker of different brain regions and different networks. Replication data denoted ‘rep.’

[0368] Figs. 11A and 11B show mean effect size in major nodes of the salience network, for amyg-based EFP (orange) and VS-EFP (blue), presented as mean across subjects ± SEM. Fig. 11A refers to the main sample and Fig. 11B refers to the replication sample, network regions based on the resting-state network atlas by (Schaefer et al., 2018).

[0369] Figs. 12A and 12B show voxels 1202 showing significantly higher (P<0.05, FDR corrected) effect sizes for amyg-based EFP than for VS-EFP were observed mainly in saliencenetwork regions (black outline 1204) and sensory (visual, auditory, somato-motor) regions (dotted outline 1206). These figures show a correlation between amyg-based EFP and salience- network related regions, also termed herein as nodes. Network region outlines based on the resting-state network atlas by (Schaefer et al., 2018).

[0370] Reference is now made to figs. 13A-13C, depicting results of nonparametric testing of the correlation distribution within functional amygdala clusters. Voxel-wise correlations between the BOLD and the amyg-based EFP were calculated along a sliding window of the movie scenes run and averaged across windows. To generate the null distribution the same procedure was repeated 100 times with shuffled data.

[0371] Fig. 13A shows results in four example participants annotated as SOI, S04, S23 and S31. The null distribution (gray, 1302) and real correlation distribution (xlOO for visualization) in a salience-related amygdala ROI (orange, 1304) and a non-salience amygdala ROI (blue, 1306). Colored (orange and blue) dashed lines denote the median of the real distribution, and gray dashed lines denote the 95th percentile of the null distribution (chance level). ROI were selected individually (left column) based on coactivation with a salience seed during the Fear clips. Dashed area 1308 in the BOLD signal denotes boundaries of the amygdala region.

[0372] Fig. 13B shows Percentage of above-chance participants in each of the ROI for each window size.

[0373] Fig. 13C shows Percentage of above-chance participants in each ROI in the test group (left) and replication (right) within the 30-TR window.

[0374] The results shown in figs. 13A-13C suggest that clusters of the amygdala that are functionally related to salience-network activity presented above-chance correlations with the amyg-based-EFP in around 70%-80% of subjects, whereas clusters that were not related to salience-network activity were correlated with amyg-EFP in up to around 50% of subjects.

[0375] Overall the results of the study show that there is a correlation between the amyg-based- EFP and BOLD signal of at least two nodes of the SN, and there is a correlation between the VS- EFP and BOLD signal of at least two nodes of the DMN network, and that distribution of the BOLD signals is distinct and separate throughout the cortex. In addition, the results show that the amyg-EFP is correlated with a BOLD signal localized in specific salience-related regions within the amygdala, more than in other non- salience-related regions of the amygdala.

[0376] As used herein with reference to quantity or value, the term “about” means “within ± 10 % of’.

[0377] The terms “comprises”, “comprising”, “includes”, “including”, “has”, “having” and their conjugates mean “including but not limited to”.

[0378] The term “consisting of’ means “including and limited to”. The term “consisting essentially of’ means that the composition, method or structure may include additional ingredients, steps and / or parts, but only if the additional ingredients, steps and / or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.

[0379] As used herein, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a compound” or “at least one compound” may include a plurality of compounds, including mixtures thereof.

[0380] Throughout this application, embodiments of this invention may be presented with reference to a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as “from 1 to 6” should be considered to have specifically disclosed subranges such as “from 1 to 3”, “from 1 to 4”, “from 1 to 5”, “from 2 to 4”, “from 2 to 6”, “from 3 to 6”, etc.; as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0381] Whenever a numerical range is indicated herein (for example “10-15”, “10 to 15”, or any pair of numbers linked by these another such range indication), it is meant to include any number (fractional or integral) within the indicated range limits, including the range limits, unless the context clearly dictates otherwise. The phrases “range / ranging / ranges between” a first indicate number and a second indicate number and “range / ranging / ranges from” a first indicate number “to”, “up to”, “until” or “through” (or another such range-indicating term) a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numbers therebetween.

[0382] Unless otherwise indicated, numbers used herein and any number ranges based thereon are approximations within the accuracy of reasonable measurement and rounding errors as understood by persons skilled in the art.

[0383] As used herein the term “method” refers to manners, means, techniques and procedures for accomplishing a given task including, but not limited to, those manners, means, techniques and procedures either known to, or readily developed from known manners, means, techniques and procedures by practitioners of the chemical, pharmacological, biological, biochemical and medical arts.

[0384] As used herein, the term “treating” includes abrogating, substantially inhibiting, slowing or reversing the progression of a condition, substantially ameliorating clinical or aesthetical symptoms of a condition or substantially preventing the appearance of clinical or aesthetical symptoms of a condition.

[0385] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

[0386] Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

[0387] It is the intent of the applicant(s) that all publications, patents and patent applications referred to in this specification are to be incorporated in their entirety by reference into the specification, as if each individual publication, patent or patent application was specifically and individually noted when referenced that it is to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority document(s) of this application is / are hereby incorporated herein by reference in its / their entirety.

Claims

WHAT IS CLAIMED IS:

1. A method for determining activity of at least one brain network, comprising: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one activity model of the activity of at least one brain network to generate an activity indication signal for activity of the at least one brain network, wherein said at least one activity model comprises at least one model of activity of a salience network (SN) or at least one model of activity of a default mode network (DMN); determining activity of the at least one brain network during said predetermined time period or a portion thereof, based on the generated activity indication signal, wherein said at least one brain network comprises the SN or the DMN.

2. A method according to claim 1, wherein said at least one activity model comprises at least two activity models including the at least one model of activity of a SN and the at least one model of activity of the DMN, and wherein said processing comprises processing the EEG signals using said at least two activity models to generate at least two activity indication signals comprising an activity indication signal of the SN and an activity indication signal of the DMN, and wherein said determining comprises determining activity of the SN and the DMN during said predetermined time period or a portion thereof, based on the at least two activity indication signals.

3. A method according to any one of claims 1 or 2, wherein said processing comprises processing the measured EEG signals using said at least one model of SN activity to generate an activity indication signal for activity of the SN, and wherein said determining comprises determining activity of the SN during said predetermined time period based on the generated activity indication signal.

4. A method according to claim 3, wherein said determining comprises determining activity of at least two brain regions of the SN, wherein said at least two brain regions are selected from a group comprising, an amygdala, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, and a rostral prefrontal cortex.

5. A method according to claim 3, wherein said determining comprises determining activity of at least two brain regions of the SN, wherein said at least two brain regions of the SN are cortical brain regions located at a brain cortex , or wherein said at least two brain regions of the SN comprise at least one cortical brain region and at least one sub-cortical brain region, or wherein said at least two brain regions of the SN comprise at least two sub-cortical brain regions.

6. A method according to any one of claims 4 or 5, wherein determining activity of the at least two brain regions of the SN comprises, calculating at least one activity value which is a measure of central tendency of activity of the at least two brain regions.

7. A method according to any one of claims 1 or 2, wherein said processing comprises processing the measured EEG signals using said at least one model of DMN activity to generate an indication of activity of the DMN, and wherein said determining comprises determining activity of the DMN during said predetermined time period based on the generated activity indication.

8. A method according to claim 7, wherein said determining comprises determining activity of at least two brain regions of the DMN, wherein said at least two brain regions comprise the ventral striatum and at least one additional brain region selected from a group comprising, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

9. A method according to claim 7, wherein said determining comprises determining activity of at least two brain regions of the DMN, wherein said at least two brain regions of the DMN are cortical brain regions located at a brain cortex, or wherein said at least two brain regions of the DMN comprise at least one cortical brain region and at least one sub-cortical brain region, or wherein said at least two brain regions of the DMN comprise at least two sub-cortical brain regions.

10. A method according to any one of claims 8 or 9, wherein determining activity of the at least two brain regions of the SN comprises, calculating at least one activity value which is a measure of central tendency of activity of the at least two brain regions.

11. A method according to any one of the previous claims, wherein said at least one activity model comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of said at least one brain network, when said at least one brain network is activated.

12. A method according to any one of the previous claims, wherein said at least one activity model is a predictor of blood oxygenation level dependent (BOLD) activity of said at least one brain network.

13. A method according to any one of the previous claims, wherein said at least one activity model comprises a ridge regression model of the at least one brain network or a portion thereof.

14. A method according to any one of the previous claims, comprising diagnosing said subject with at least one disorder, based on the determined activity of the at least one brain network.

15. A method according to any one of claims 1 to 13, wherein said subject is prediagnosed with at least one disorder, and wherein said method comprises selecting a treatment for said at least one disorder based on said determined activity of the at least one brain network.

16. A method according to any one of claims 1 to 13, wherein said subject receives at least one treatment for at least one disorder, and wherein said method comprising determining an efficacy of said treatment based on the determined activity of the at least one brain network.

17. A method according to any one of claims 15 or 16, wherein said treatment comprises at least one of, drug treatment, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT), biofeedback or neurofeedback.

18. A method according to any one of claims 14 to 16, wherein said at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

19. A method for determining activity of at least two brain networks, comprising: receiving signals recorded during a predetermined time period from a subject brain using at least one electrode; measuring EEG signals from the received signals; processing the measured EEG signals using at least one first activity model of the activity of a salience network (SN) and at least one second activity model of the activity of a default mode network (DMN), to generate a first activity indication signal for the activity of the SN and a second activity indication signal for the activity of the DMN; determining activity of the SN and the DMN during said predetermined time period or a portion thereof, based on said first activity indication signal and said second activity indication signal; determining a relation between activity of the SN and the activity of the DMN during said predetermined time period or a portion thereof, based on said determined activity.

20. A method according to claim 19, wherein said determining a relation comprises determining a relation between amplitudes of the SN and DMN activity over a predetermined time period, between length of epochs in which activity of the DMN and SN changes, between magnitude of changes in the activity of the SN and DMN over a predetermined time period, and / or between any parameter of a first signal indicating SN activity and a second signal indicating DMN activity.

21. A method according to any one of claims 19 or 20, comprising: diagnosing said subject with at least one disorder based on said determined activity and / or said determined relation.

22. A method according to any one of claims 19 or 20, wherein said subject is prediagnosed with at least one disorder, and wherein said method comprising selecting at least one treatment for said at least one disorder based on said determined activity and / or said determined relation.

23. A method according to any one of claims 19 or 20, wherein said subject is diagnosed with at least one disorder and receives at least one treatment for the at least onedisorder, and wherein said method comprising determining an efficacy of said at least one treatment based on said determined activity and / or said determined relation.

24. A method according to any one of claims 22 or 23, wherein said at least one treatment comprises at least one of, drug treatment, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT), biofeedback or neurofeedback.

25. A method according to any one of claims 21 to 24, wherein said at least one disorder comprises a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

26. A system for determining activity of at least one brain network, comprising: a memory, wherein said memory stores signals recorded from a subject brain using at least one electrode, and at least one model of activity of at least one brain network, wherein said at least one activity model comprises an activity model of a salience network (SN) or an activity model of a default mode network (DMN); a user interface; a control circuitry, wherein said control circuitry is configured to: measure EEG signals from the signals stored in said memory; process the EEG signals using said at least one activity model comprising said activity model of the salience network (SN) or said activity model of the default mode network (DMN), to generate an activity indication signal for the activity of the at least one brain network; determine activity of the at least one brain network based on said generated activity indication signal, wherein said at least one brain network comprises said SN or said DMN; signal said user interface to generate an indication with information about the determined activity.

27. A system according to claim 26, wherein said at least one model of activity comprises said activity model of the SN and said activity model of said DMN;wherein said control circuitry is configured to process said EEG signals using said activity model of the SN and said activity model of said DMN to generate a first activity indication signal for SN activity, and a second activity indication signal for DMN activity, determine activity of said SN and said DMN based on said first activity indication signal and said second activity indication signal.

28. A system according to any one of claims 26 or 27, wherein said control circuitry is configured to determine activity of at least two brain regions of the SN based on said generated activity indication signal, wherein said at least two brain regions are selected from a group including, amygdala, anterior insula, dorsal anterior cingulate cortex, supramarginal gyrus, rostral prefrontal cortex, and the amygdala.

29. A system according to any one of claims 26 or 27, wherein said control circuitry is configured to determine activity of at least two brain regions of the DMN based on said generated activity indication signal, wherein said at least two brain regions are selected from a group comprising, a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

30. A system according to any one of claims 26 to 29, wherein said memory stores clinical and / or medical information on said subject, and wherein said control circuitry is configured to generate a diagnosis indication indicating a diagnosis of said subject with at least one disorder based on said clinical and / or medical information and based on said determined activity of said at least one brain network.

31. A system according to claim 30, wherein said control circuitry is configured to signal said user interface to generate a human detectable indication with information about said diagnosis indication.

32. A system according to any one of claims 26 to 29, wherein said memory stores information about at least one disorder of said subject, and a plurality of treatments for said at least one disorder, and wherein said control circuitry is configured to select at least one treatment out from said plurality of treatments based on said stored information and said determined activity of the at least one brain network.

33. A system according to claim 32, wherein said control circuitry is configured to signal said user interface to generate a human detectable indication with information about said selected treatment.

34. A system according to any one of claims 26 to 33, wherein said memory stores information about at least one treatment provided to the subject, and wherein said control circuitry is configured to determine an efficacy of said treatment based on said stored information and said determined activity of the at least one brain network.

35. A system according to claim 34, wherein said control circuitry is configured to signal said user interface to generate a human detectable indication with information about said determined efficacy.

36. A system according to any one of claims 30 to 33, wherein said at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abuse disorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

37. A system according to any one of claims 32 to 35, wherein said at least one treatment comprises least one of, drug treatment, neurofeedback, psychotherapy treatment, behavioral treatment, cognitive behavioral treatment (CBT).

38. A system according to any one of claims 26 to 37, wherein said at least one model of activity stored in said memory comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of said at least one brain network, when said at least one brain network is activated.

39. A system for determining activity of at least two brain networks, comprising: a memory, wherein said memory stores signals recorded from a subject brain using at least one electrode, at least two activity models comprising at least one activity model of a salience network (SN) and at least one activity model of the default mode network (DMN);a control circuitry, wherein said control circuitry is configured to: measure EEG signals from the signals stored in said memory; process the EEG signals using said at least two activity models to generate at least two activity indication signals comprising a first activity indication signal for the activity of the SN and a second activity indication signal for the activity of the DMN; determine activity of the SN based on said first activity indication signal and determining activity of the DMN based on said second activity indication signal; generate an indication with information about said activity of the SN and activity of said DMN.

40. A system according to claim 39, wherein said control circuitry is configured to determine a relation between at least one activity parameter of the determined SN activity and the at least one activity parameter of the determined DMN activity, wherein said at least one activity parameter comprises at least one of, amplitude, timing, and duration.

41. A system according to any one of claims 39 or 40, comprising a user interface, and wherein said control circuitry is configured to signal said user interface to generate and deliver at least one human detectable indication indicating said determined activity of the SN and said determined activity of the DMN and / or said determined relation.

42. A system according to any one of claims 39 to 41, wherein said at least one activity model of the SN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of said SN or at least two brain regions of the SN, when said SN or said at least two brain regions of the SN are activated, and wherein said at least one activity model of the DMN comprises a model of functional magnetic resonance imaging (fMRI) spatial scan data of said DMN or at least two brain regions of the DMN, when said DMN or said at least two brain regions of the DMN are activated.

43. A method for delivery of neurofeedback training, comprising: selecting at least one sensory stimulus expected to activate at least one brain network in a subject brain, wherein said at least one brain network comprises a salience network (SN) and / or a default mode network (DMN); delivering said at least one sensory stimulus to said subject;recording electrical signals generated by the brain of said subject by at least one electrode, in conjunction with said delivering; processing said recorded electrical signals to determine an activation level of at least two brain regions of the SN and / or at least two brain regions of the DMN; modifying said at least one stimulus according to said determined activation level and delivering said modified stimulus to said subject; repeating said delivering said recording, said processing, and said modifying.

44. A method according to claim 43, wherein said at least two brain regions of the SN are selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala, and wherein said at least two brain regions of the DMN are selected from a group comprising wherein said at least two brain regions are selected from a group comprising, a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus.

45. A method according to any one of claims 43 or 44, wherein said processing comprises processing the recorded electrical signals to determine a relation between said activation level of said at least two brain regions of the SN and / or of the DMN in response to said at least one stimulus, and a baseline activation level of said at least two brain regions of the SN and / or of the DMN in response to at least one neutral stimulus selected not to activate the SN and / or the DMN, and wherein said modifying comprises modifying said at least one stimulus according to said determined relation.

46. A method according to any one of claims 43 to 45, wherein said subject is diagnosed with at least one disorder associated with irregular activity of the SN and / or of the DMN, and wherein said neurofeedback training is configured to teach said subject to modify said irregular activity of the SN and / or of the DMN towards a regular reactivity of the SN and / or of the DMN.

47. A method according to claim 46, wherein said at least one disorder comprises at least one of, a disorder of social-emotional function, anxiety disorders, post-traumatic stress disorder (PTSD), dissociative sub-type of PTSD, schizophrenia, frontotemporal dementia, bipolar disorder, major depression, attention-deficit / hyperactivity, autism spectrum, a substance abusedisorder, obsessive compulsive disorder (OCD), pain disorders, fibromyalgia and / or Alzheimer’s disease (AD).

48. A system for delivery of neurofeedback training, comprising: a memory, wherein said memory stores at least one sensory stimulus selected to activate a salience network (SN) and / or the default mode network (DMN) in a subject, and at least one activity model of activity of the SN and / or at least one activity model of activity of the DMN; a user interface; a control circuitry, wherein said control circuitry is configured to: signal said user interface to deliver said at least one stimulus to said subject; receive electrical signals recorded by at least one electrode from the subject brain during the delivery of said stimulus; measure EEG signals from said received electrical signals; process the EEG signals using said at least one activity model of the SN and / or using the at least one activity model of the SN, to generate a first activity indication signal for the activity of the SN and / or a second activity indication signal for the activity of the DMN; determine activity of the SN and / or activity of the DMN based on said generated activity indication signal; signal said user interface to modify said at least one stimulus according to said determined activity of the SN and / or of the DMN and to deliver said modified stimulus to said subject.

49. A system according to claim 48, wherein said control circuitry is configured to determine activity of at least two brain regions of the SN based on said generated first activity indication signal, and / or to determine activity of at least two brain regions of the DMN based on said generated second activity indication signal.

50. A system according to claim 49, wherein said at least two brain regions of the SN are selected from a group comprising, an anterior insula, a dorsal anterior cingulate cortex, a supramarginal gyrus, a rostral prefrontal cortex, and an amygdala, or portions thereof, and / or wherein said at least two brain regions of the DMN are selected from a group comprising a ventral striatum, a posterior cingulate cortex, a medial prefrontal cortex, an inferior parietal lobe, a middle frontal gyrus, a middle temporal gyrus, and a hippocampus, or portions thereof.

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