1. The science behind Nalysis (NeuroAnalysis )

2. 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

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. 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

5. 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

6. 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

7. 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
Constraint
Frustration 1 2
Connectivity
segregation
Plasticity
De-optimization
Plasticity
Optimization
Connectivity
Integration
Disturbed Resting-state
default mode network Frustration

8. In short (simplified) CBP argues the following translation
For a more detailed explanation of CBP go to
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

9. 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

10. 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

11. 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

12. The platform uses a 6-dimentional framework to map users clinicians and cyber-wearable inputs1 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

13. 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

14. 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,

15. 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

16. 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)

17. 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

18. 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
s content
Speech volume
Text Volume
s Volume
Speech Rhythm during day consistency
Text Rhythm during day consistency
s Rhythm during day consistency
Speech Speed
Text Speed
s Speed
Speech Out>in Out <in
Text Out>in Out <in
s 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

19. 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

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. 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

22. 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 – “