1. The science behind Nalysis (NeuroAnalysis )

2. Psychiatric diagnosis DESCRIPTIVE Composed from: Symptoms (complaints) signs (observations) Example: Depression Medical diagnosis ETIOLOGICAL The cause of the disease Place, body location Diseased process, pathology Example: Appendicitis In Medicine diagnosis is Etiological, the name of disease is composed of location and pathology, in psychiatry diagnosis is descriptive composed of symptoms, this is because the causes of psychiatric disorders are unknown.

3. Not knowing the causes of mental disorders impedes their cure To effectively treat mental disorders we should first reformulate them as brain disorders and validate (discover) their brain-related origins First step is to TRANSLATE phenomenology into brain disturbances P h e n o m e n o l o g y Brain T R A N S L A T E

4. P h e n o m e n o l o g y Brain The RDoC (Research Domain Criteria) is an Attempt by the previous INMH director Tom Insel to TRANSLATE psychiatric phenomenology into brain- related disturbances Translate

5. P h e n o m e n o l o g y Brain Network Disturbances The CBP (Clinical Brain Profiling) hypothesis proposes to concentrate on the “Circuit” level of brain organization and translate phenomenology into related-brain network disturbances

6. Signs Symptoms History P h e n o m e n o l o g y CBP proposes that phenomenology of psychiatric disorders can be translated / defined by a vector of 6 entries Connectivity segregation Connectivity Integration Plasticity Optimization Plasticity De-optimization Constraint Frustration Disturbed Resting-state default mode network Frustration Translate

7. 1 2 Connectivity segregation Connectivity Integration Plasticity Optimization Plasticity De-optimization Constraint Frustration Disturbed Resting-state default mode network Frustration Connectivity segregation Connectivity Integration Plasticity Optimization Plasticity De-optimization Constraint Frustration Disturbed Resting-state default mode network Frustration CBP can be presented as a graph vector as in http://neuroanalysis.org.il/cbp/index.php http://neuroanalysis.org.il/cbp/index.php CBP can be presented as a round graph vector as in BP http://neuroanalysis.org.il/?page_id=1675 http://neuroanalysis.org.il/?page_id=1675

8. For a more detailed explanation of CBP go to https://www.youtube.com/watch?v=JDprKsYgiXQ&feature=youtu.be https://www.youtube.com/watch?v=JDprKsYgiXQ&feature=youtu.be In short (simplified) CBP argues the following translation Connectivity segregation Connectivity Integration Plasticity Optimization Plasticity De-optimization Constraint Frustration Disturbed Resting-state default mode network Frustration Psychosis Negative signs sch Mania Depression Anxyety Personality disorders P h e n o m e n o l o g y Brain T R A N S L A T E

9. Signs Symptoms History P h e n o m e n o l o g y To Validate CBP Sensors and wearables are required. Sensors collect the phenomenology and Brain-imaging EEG wearable synchronously monitors phenomenology-related brain disturbances Translate phenomenology

10. Signs Symptoms History P h e n o m e n o l o g y Validation will require finding causal relationships between phenomenology and brain disturbances this will require machine learning and theory driven efforts (theory being the CBP hypothesis) CBP Theory Driven phenomenology Deep Learning

11. User Cyber + Wearables User User Clinician Consumer EEG Wearables Consumer tDCS Wearables Cognitive Training Neurofeedback Psychotherapy Tele-Psychiatry Big-data CBP validation can be achieved with a commercially-valid medical platform combining data collection sensors wearables and machine- learning of large-data algorithms, changing the way end-users clinicians and patients practice psychiatry

12. 1 1 2 0 2 3 4 5 6 Legend : 0= non 1=Partial 2=Full The platform uses a 6-dimentional framework to map users clinicians and cyber-wearable inputs User Clinician User Cyber + Wearables User rates his subjective complaints (symptoms) User clinician rates his objective (signs) assessments Collected phenomenology from cyber activity and sensors rates the dimensions

13. Derealization, Confused “Empty head,” Repeated reduced thoughts. Elated, Excited, Restless, Depressed, Tiered, Heavy, Apathetic. Feels sensitive very much influenced by social events Anxious, panic, fear of dying Palpitation, breathing, dizziness, tremor D O CF Cs Htd Ci Hbu DMN 1 2 Legend : 0= non 1=Partial 2=Full 0 6-D of Subjective Experience by the Patient

14. DimensionSubjective0= non1=partial2=Full DMN Feels sensitive very much influenced by social events non Sometimes Approximately half of any given period All the time Ci Hbu“Empty head,” repeated reduced thoughts.non““ Cs HtdDerealization, Confusednon““ CF Anxious, panic, fear of dying Palpitation, breathing, dizziness, tremor non““ DDepressed, Tiered, Heavy, Apatheticnon““ OElated, Excited, Restless,non““ 6-D of Subjective Experience by the Patient

15. D O CF Cs Htd Ci Hbu DMN 1 2 0 Psychosis Positive signs Loos associations Delusions Hallucinations Negative signs Poverty of speech Perseverations Flattening affect Concrete Reduced cognition Depression Phenomenology Manic Phenomenology Anxiety Phenomenology Legend : 0= non 1=Partial 2=Full 6-D of Clinician Assessment Mental Status Examination Introvert / Shay Obsessive / rigid 1 2 Extrovert / Narcissistic Impulsive / un-directed

16. DimensionHere use DSM criteria for major entities0= non1=partial2=Full DMN Immaturity (low frustration threshold, egocentricity, dependence) use Narcissistic, Borderline, DSM and psychoanalytic criteria non Introvert / Shay Obsessive / rigid Extrovert / Narcissistic Impulsive / un-directed Ci HbuNegative signs (as in DSM)non Sometimes Approximately half of any given period All the time Cs HtdPositive signs (as in DSM)non““ CFAnxiety (as in DSM)non““ DMajor depression (as in DSM)non““ OMania (as in DSM)non““ 6-D of Clinician Assessment Mental Status Examination

17. D O CF Cs Htd Ci Hbu DMN 1 2 0 Movements > Inconsistencies CA Speech disorganized Movements < Reduced CA Reduced speech repeats. Activity > Oscillometer > Speech > CA > CA social Networks Type of searches Visits Legend : >=Increase <=Decrease CA= Cyber-activity Activity < Oscillometer < Speech < CA < Activity < Oscillometer > 6-D of Cyber-Activity Wearable Sensors

18. CollectedCs HtdCi HbuDOCFDMN1= Introvert1= Rigid/Obsessive2= Extrovert2= Impulsive Speech content Text content Emails content Speech volume Text Volume Emails Volume Speech Rhythm during day consistency Text Rhythm during day consistency Emails Rhythm during day consistency Speech Speed Text Speed Emails Speed Speech Out>in Out <in Text Out>in Out <in Emails Out>in Out <in Number of Phone contacts Number of Text contacts) Time spent actively in social network activity per day Time spent in social network activity compared with non-social activity Web Content / Type of site Web Content / Variability contents (same contents) Repeated sites % Time spent in each site Consistency Mobile Oscillometer Mobile Navigation speed Mobile Navigation location Mobile Navigation location repeats Mobile Speech Voice volume Mobile Speech Voice tone food consuption body weight pulse breathing day overall routeen day lesure routeen Age onset of changes The above = Stable - fluctuating Progressive provisional # 62/252,596 Cyber-Activity Wearable Sensors Collections

19. DOCFCs HtdCi HbuDMN 1 2 0 Objective Clinician Subjective Patient Objective Sensors Hamming distance calculated for Confidence Coefficient of all dimensions for each measurement Comparison of the subjective rating of the user, the objective scoring of the clinician and the collected estimations of the cyber-sensors data are constantly compared ( Hamming distance) to generate a “confidence coefficient of cross validation corroborating the findings toward dicovery

20. Brain Imaging biomarker will be collected by using the 6-Dmentional subjective, objective and cyber-related application concomitantly synchronized wearing an EEG consumer device phenomenology

21. D O CF Cs Htd Ci Hbu DMN 1 2 0 6-D of Discoverable Brain-Imaging Biomarkers Correlations, Synchrony, Granger causality Mutual information, Dimension estimation Bayesian statistics Dynamic Causal Modeling Independent components analysis, Neural complexity (Correlation matrix) Graph assessment of overall Small Wordiness. Estimating hierarchy with hub composition K-shell decomposition, Fractal geometry estimations, Integrated information theory estimations Whole brain Matching complexity, estimations. Free energy estimations. Entropy measurements Age-related changes of all of the above Stability over millisecond ranges of all below

22. DimensionBrain-Imaging Biomarkers Brain profiles DMN Ci Hbu Connectivity integration (Ci) Hierarchal bottom up insufficiency (Hbu) Over-connection – As seen in Correlations, Synchrony, Granger causality Mutual information, Dimension estimation Bayesian statistics Dynamic Causal Modeling Independent components analysis, Neural complexity (Correlation matrix) Graph assessment of overall Small Wordiness Cs Htd Connectivity segregation (Cs) Hierarchal Top-down shift (Htd) Disconnection – As seen in - “ CFInstabilities within millisecond range. As seen in - “ D Deoptimization - Whole brain Matching complexity, estimations. Free energy estimations. Entropy measurements OOptimization – “ 6-D of Discoverable Brain-Imaging Biomarkers