The science behind Nalysis (NeuroAnalysis )

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. 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

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

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

Brain Network Disturbances The CBP (Clinical Brain Profiling) hypothesis proposes to concentrate on the “Circuit” level of brain organization and translate phenomenology into brain-related network disturbances Brain Network Disturbances 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 default mode network Frustration Disturbed Resting-state Connectivity segregation Connectivity Integration Plasticity De-optimization Plasticity Optimization Constraint Frustration Signs P h e n o m e n o l o g y Translate Symptoms History

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

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

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

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) Signs phenomenology P h e n o m e n o l o g y Symptoms Deep Learning CBP Theory Driven History

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 Big-data User Clinician User Tele-Psychiatry Psychotherapy Consumer EEG Wearables User Cyber + Wearables Neurofeedback Cognitive Training Consumer tDCS Wearables

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

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

6-D of Subjective Experience by the Patient Dimension Subjective 0= non 1=partial 2=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. “ Cs Htd Derealization, Confused CF Anxious, panic, fear of dying Palpitation, breathing, dizziness, tremor D Depressed, Tiered, Heavy, Apathetic O Elated, Excited, Restless,

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

6-D of Clinician Assessment Mental Status Examination Dimension Here use DSM criteria for major entities 0= non 1=partial 2=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 Hbu Negative signs (as in DSM) Sometimes Approximately half of any given period All the time Cs Htd Positive signs (as in DSM) “ CF Anxiety (as in DSM) D Major depression (as in DSM) O Mania (as in DSM)

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

Cyber-Activity Wearable Sensors Collections  Collected Cs Htd Ci Hbu D O CF DMN 1= Introvert 1= Rigid/Obsessive 2= Extrovert 2= 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 Cyber-Activity Wearable Sensors Collections provisional # 62/252,596

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 discovery D O CF Cs Htd Ci Hbu DMN 1 2 Objective Clinician Subjective Patient Objective Sensors

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

CF Cs Htd D O Ci Hbu DMN 6-D of Discoverable Brain-Imaging Biomarkers Stability over millisecond ranges of all below D O CF Cs Htd Ci Hbu DMN 1 2 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

6-D of Discoverable Brain-Imaging Biomarkers Dimension Brain-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 - “ CF Instabilities within millisecond range. As seen in - “ D Deoptimization - Whole brain Matching complexity, estimations. Free energy estimations. Entropy measurements O Optimization – “