Exciting Times Computational Psychiatry Digital Mental Health

Psychiatric diagnosis

I am depressed, sad, feel helpless, despaired, what do I have doctor ? Pleas tell me

You have depression

Gee – isn’t that what I just told you Can’t you give me any added value

?!?

I am depressed, what do I have doctor ? Pleas tell me You have depression Place in the body What happend to it It is infected How to treat it Antibiotics My stomach hurts, what do I have doctor ? You have Appendecitis

? Urologist Cardiologist Gastroenterologist Psychiatrist

Urologist Cardiologist Gastroenterologist Psychiatrist

1973

US UK

Checklist Depressed Insomnia Pessimism Anorexia Etc ….. Allen Frances MD Michael First MD Checklist Depressed Insomnia Pessimism Anorexia Etc ….. Inter-rater Reliability

Currently No Etiology (not brain related) No Category & Not Personalized No Pharmacology (no med’ efficacy) Hampered Research (no advance) Brain Profiler © All rights resaved August 2017

This is what Tom Insel had to say about the DSM Previous head of NIMH Moller HJ et al. DSM 5 reviewed Eur Arch Psychiatry Clin Neurosci. 2015 Cuthbert BN , & Insel TR Toward the future of psychiatric diagnosis BMC Med. 2013 Collins PY1, Insel TR, Chockalingam A, Daar A, Maddox YT. Grand challenges in global mental health PLoS Med. 2013 Tomason T 2014 file:///C:/Users/Peled/Downloads/fulltext_stamped.pdf

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Biological Psychiatry

RDoC

Biological Psychiatry RDoC

RDoC

Biological Psychiatry Explanatory Power

Computational Psychiatry Biological

Computational Psychiatry Biological

Computational Psychiatry Biological Connectivity Plasticity DNM

Example for Computational psychiatry

- + Hallucinations Loosening Delusions restlessness Positive symptoms  BLEULER 1920 Mental disorganization Affect Associations Ambivalence Autism 4 A’s Schizophrenia Schizo - Prenus Hallucinations Loosening Delusions restlessness Poverty thought Perseverations Avolition + - Positive symptoms 1900 KRAEPLIN Dementia praecox Based on prognosis Negative symptoms Function Age 18

Hopfield type architecture

Formulate brain dynamics as a physical state-space dynamic system

Positive symptoms Negative symptoms

Psychological Event Computational Psychiatry Biological Biological Event

Clinical Brain Profiling

What do we know about brain organization Connectivity Plasticity DNM Disorders = Perturbation Connectivity Plasticity DNM

Connectivity segregation (Cs) De- Optimization (D) Psychosis De- Optimization (D) Depression Connectivity integration (Ci) Negative signs Optimization (O) Mania Connectivity Plasticity Constraint frustration (CF) Anxiety Hierarchical bottom-up insufficiency (Hbu) Avolition DNM Stimulus bound CF(b) Phobia Hierarchical top-down shift (Htd) Delusions Hallucinations

Psychosis and schizophrenia disorders

Connectivity segregation (Cs) Psychosis Connectivity integration (Ci) Negative signs Connectivity Plasticity Hierarchical bottom-up insufficiency (Hbu) Avolition DNM Hierarchical top-down shift (Htd) Delusions Hallucinations

- + Hallucinations Loosening Delusions restlessness Positive symptoms  BLEULER 1920 Mental disorganization Affect Associations Ambivalence Autism 4 A’s Schizophrenia Schizo - Prenus Hallucinations Loosening Delusions restlessness Poverty thought Perseverations Avolition + - Positive symptoms 1900 KRAEPLIN Dementia praecox Based on prognosis Negative symptoms Function Age 18

Disconnection Syndrome Psychosis The associations of an adult ego could be temporarily or permanently weakened, with a similar result of random or confused thought processes. … resulting in psychotic states. Theodore Meynert (1833-1892) Disconnection Syndrome Psychosis

Disconnection Syndrome Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci. 2010 Nov 24;30(47):15915-26. van den Heuvel MP, Mandl RC, Stam CJ, Kahn RS, Hulshoff Pol HE our findings suggest that schizophrenia patients have a less strongly globally integrated structural brain network with a reduced central role for key frontal hubs, resulting in a limited structural capacity to integrate information across brain regions Yu Q, Sui J, Rachakonda S, He H, Pearlson G, Calhoun VD. Altered small-world brain networks in temporal lobe in patients with schizophrenia performing an auditory oddball task. Front Syst Neurosci. 2011 Feb 8;5:7. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: Where are we now? Neurosci Biobehav Rev. 2011 Apr;35(5):1110-24.Review Guye M, Bettus G, Bartolomei F, Cozzone PJ. Theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks. MAGMA. 2010 Dec;23(5-6):409-21. Epub 2010 Mar 27.Graph Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci. 2008 Sep 10;28(37):9239-48. Bodnar M, Harvey PO, Malla AK, Joober R, Lepage M. The parahippocampal gyrus as a neural marker of early remission in first-episode psychosis: a voxel-based morphometry study. Clin Schizophr Relat Psychoses. 2011 Jan;4(4):217-28. Jones MW. Errant ensembles: dysfunctional neuronal network dynamics in schizophrenia. Biochem Soc Trans. 2010 Apr;38(2):516-21. Review Psychosis Disconnection Syndrome

Negative symptoms Positive symptoms Giulio Tononi Order Randomness Complexity Neural Complexity Disconnection dynamics Over-connection dynamics Negative symptoms Positive symptoms 45

Negative symptoms Positive symptoms

Avolition Negative symptoms Positive symptoms Delusions Joaquin Fuster

Bernard Baars Marcel Mesulam Stanislav Dehene Giulio Tononi Hallucinations Giulio Tononi

Clinical Brain Profiling (CBP) Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) Connectivity segregation (Cs) Psychosis Connectivity integration (Ci) Negative signs Connectivity Plasticity Hierarchical bottom-up insufficiency (Hbu) Avolition DNM Hierarchical top-down shift (Htd) Delusions Hallucinations

Mood disorders

Connectivity segregation (Cs) De- Optimization (D) Psychosis De- Optimization (D) Depression Connectivity integration (Ci) Negative signs Optimization (O) Mania Connectivity Plasticity Constraint frustration (CF) Anxiety Hierarchical bottom-up insufficiency (Hbu) Avolition Stimulus bound CF(b) Phobia Hierarchical top-down shift (Htd) Delusions Hallucinations

Plasticity Environment SSRI Mood Depression Anxiety Optimization Adaptability Free energy

Clinical Brain Profiling (CBP) Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) Plasticity Optimization (O) Mania De- Optimization (D) Depression Constraint frustration (CF) Anxiety Stimulus bound CF(b) Phobia

Personality disorders

Connectivity segregation (Cs) De- Optimization (D) Psychosis De- Optimization (D) Depression Connectivity integration (Ci) Negative signs Optimization (O) Mania Connectivity Plasticity Constraint frustration (CF) Anxiety Hierarchical bottom-up insufficiency (Hbu) Avolition DNM Stimulus bound CF(b) Phobia Hierarchical top-down shift (Htd) Delusions Hallucinations

Default Mode Network Environment

Default Mode Network Environment Hebbian Dynamics Personality

Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) DNM

Brain Profiler

Brain Related Psychiatric Diagnosis Bain Profiler © All rights resaved August 2017

Cs connectivity segregation All of the various brain disturbances are mapped onto a 9 axis profile “the Clinical Brain Profile (CBP) Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) Cs connectivity segregation Optimal brain Disturbance to plasticity and Optimization Ci connectivity integration Htd Hierarchal top down shift Hbu Hierarchal Bottom-up insufficiency D De-optimization O Hyper- optimization CF Constrain Frustration CF(b) Constrain Bound Default Mode Network to connectivity

Clinical Brain Profiling The Clinical Brain Profile (CBP) of the patient looks like this: Millisecond range activity Plasticity over weeks and months Lifetime Developmental Plasticity DNM Connectivity Cs Connectivity segregation Ci Connectivity integration Hbu Hierarchical bottom-up insufficiency Htd Hierarchical top-down shift O Optimization D De- Optimization CF Constraint frustration CF(b) Stimulus bound DMN Default Mode Network Negative signs Mania Delusions Hallucinations Avolition Psychosis Depression Anxiety Phobia Personality disorder Clinical Brain Profiling

Unsupervised fuzzy clustering of CBP into two categories classifies patients based on clinical severity validating CBP sensitivity to clinically meaningful phenomenology.

Total N= 642 Corr = 0.56 Corr = 0.8 Corr = 0.87 Corr = 0.63 Schizophrenia N= 304 Archetype schizophrenia positive signs Axis Y= Percentages Axis X : 1 = DMN Default Mode Network 2= D De-optimization 3 = O Optimization 4 = CF Constraint Frustration 5= CF Constraint Frustration bound 6 = Cs Connectivity segregation 7= Ci Connectivity integration 8= Hbu Hierarchical bottom-up 9 = Htd Hierarchical top- down Corr = 0.56 Archetype schizophrenia Negatives signs Corr = 0.8 Schizoaffective N=34 Archetype Schizoaffective Corr = 0.87 Total N= 642 Depression N=59 Archetype Depression Corr = 0.63 Anxiety N=94 Archetype Anxiety Corr = 0.66 Archetype Personality disorder Personality Disorders N=40 Others N=111 Corr = 0.9

The Brain Profiler Site

Brain Profiler is a Computer and an App platform Brain Profiler © All rights resaved August 2017

EEG Subjective Condition Objective digital Psychiatrist Assessment Powerful Statistics

COMPUTATIONAL PSYCHIATRY Clinician Scale pheno CBP Unique Patient Symptoms CBP Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) CBP Unique Cyber Mob sensors Obj’ digital CBP Brain Imaging VALID CBP Unique Personalized Tele-medicine Alerts & Notifications Queuing and Management Treatment-Response EMR

Signal Processing & Validation

EEG Attractor signals can be informative about chaotic and periodic dynamics of disconnection and over-connection activity respectively. They can also inform about general stability of the brain systems

Calculating correlation matrices can inform us about brain connectivity by constructing Graphs of connectivity patterns. These can be evaluated over different and extended time-scales.

Graphs can help estimate Small-World optimal brain networks as well as fixed overly connected or randomly disconnected networks. SMALL WORLD NETWORK

Node attacks (random versus degree-related) can measure network reliance and vulnerability to functional perturbations Brain Profiler © All rights resaved August 2017

Path length = inversely related to global efficacy of parallel information transfer Clustering Coefficient = Measure of density of connections between nearest neighbors Small-worldness = high clustering small path length comparable to random graph Node = cortical region or neuron Hierarchy = Hubs with many long distance connections and few local connections Edge = statistical measure of association Mean connection distance = Euclidean distance between centers in stereotactic space Degree = the number of edges connecting a node Assortativity = measures the preference of a node to connect to other nodes of similar degree Rent exponent = topo-physical embedment in physical space of the network, scaling relationships

Dynamic Causal Modeling (DCM) can be specifically relevant to estimating error predictions in hierarchical systems, and matching dynamics with free energy reduction within hierarchies. Brain Profiler © All rights resaved August 2017

Contact information neuroanalysis@gmail.com Brain Profiler © All rights resaved August 2017