Exciting Times. Computational Psychiatry. Digital Mental Health
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1
2 Exciting Times Computational Psychiatry Digital Mental Health
3 Psychiatric diagnosis
4 I am depressed, sad, feel helpless, despaired, what do I have doctor? Pleas tell me
5 You have depression
6 Gee isn t that what I just told you Can t you give me any added value
7 ?!?
8 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 My stomach hurts, what do I have doctor? You have Appendecitis How to treat it Antibiotics
9 ? Urologist Cardiologist Gastroenterologist Psychiatrist
10 Urologist Cardiologist Gastroenterologist Psychiatrist
11 1973
12 US UK
13 Allen Frances MD Michael First MD Checklist 1. Depressed 2. Insomnia 3. Pessimism 4. Anorexia 5. Etc..
14
15 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
16 This is what Tom Insel had to say about the DSM Tom Insel Previous head of NIMH Moller HJ et al. DSM 5 reviewed Eur Arch Psychiatry Clin Neurosci Cuthbert BN, & Insel TR Toward the future of psychiatric diagnosis BMC Med Collins PY 1, Insel TR, Chockalingam A, Daar A, Maddox YT. Grand challenges in global mental health PLoS Med Tomason T 2014 file:///c:/users/peled/downloads/fulltext_stampe d.pdf
17 Translate
18 Biological Psychiatry
19 RDoC
20 Biological Psychiatry RDoC
21 RDoC
22 Biological Psychiatry Explanatory Power
23 Biological Psychiatry Computational Psychiatry
24 Biological Psychiatry Computational Psychiatry
25 Connectivity Plasticity Computational Psychiatry DNM Biological Psychiatry
26 Example for Computational psychiatry
27 Positive symptoms Hallucinations Loosening Delusions restlessness + - BLEULER 1920 Mental disorganization Affect Associations Ambivalence Autism 4 A s Schizophrenia Schizo - Prenus Poverty thought Perseverations Avolition Negative symptoms 1900 KRAEPLIN Dementia praecox Based on prognosis Function Age 18
28
29
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32 Hopfield type architecture
33 Formulate brain dynamics as a physical state-space dynamic system
34 Positive symptoms Negative symptoms
35
36 Psychological Event Computational Psychiatry Biological Event Biological Psychiatry
37 Clinical Brain Profiling
38 What do we know about brain organization Connectivity Plasticity DNM Disorders = Perturbation Connectivity Plasticity DNM
39 Connectivity segregation (Cs) Psychosis De- Optimization (D) Depression Connectivity integration (Ci) Negative signs Optimization (O) Mania Hierarchical bottom-up insufficiency (Hbu) Avolition Connectivity Plasticity Constraint frustration (CF) Anxiety Hierarchical top-down shift (Htd) Delusions Hallucinations DNM Stimulus bound CF(b) Phobia
40 Psychosis and schizophrenia disorders
41 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
42 Positive symptoms Hallucinations Loosening Delusions restlessness + - BLEULER 1920 Mental disorganization Affect Associations Ambivalence Autism 4 A s Schizophrenia Schizo - Prenus Poverty thought Perseverations Avolition Negative symptoms 1900 KRAEPLIN Dementia praecox Based on prognosis Function Age 18
43 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 ( ) Psychosis Disconnection Syndrome
44 Jones MW. Errant ensembles: dysfunctional neuronal network dynamics in schizophrenia. Biochem Soc Trans Apr;38(2): Review Psychosis Disconnection Syndrome
45 Complexity Neural Complexity Giulio Tononi Randomness Order Over-connection dynamics Disconnection dynamics Negative symptoms Positive symptoms
46
47 Negative symptoms Positive symptoms
48 Avolition Negative symptoms Positive symptoms Delusions Joaquin Fuster
49 Bernard Baars Hallucinations Marcel Mesulam Stanislav Dehene Giulio Tononi
50 Cs Ci Htd Hbu D O CF CF(b) DMN Connectivity integration (Ci) Negative signs Connectivity segregation (Cs) Psychosis Clinical Brain Profiling (CBP) Hierarchical bottom-up insufficiency (Hbu) Avolition Connectivity Plasticity DNM Hierarchical top-down shift (Htd) Delusions Hallucinations
51 Mood disorders
52 Connectivity segregation (Cs) Psychosis De- Optimization (D) Depression Connectivity integration (Ci) Negative signs Optimization (O) Mania Hierarchical bottom-up insufficiency (Hbu) Avolition Connectivity Plasticity Constraint frustration (CF) Anxiety Hierarchical top-down shift (Htd) Delusions Hallucinations Stimulus bound CF(b) Phobia
53 Plasticity Environment
54 Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) De- Optimization (D) Depression Optimization (O) Mania Plasticity Constraint frustration (CF) Anxiety Stimulus bound CF(b) Phobia
55 Personality disorders
56 Connectivity segregation (Cs) Psychosis De- Optimization (D) Depression Connectivity integration (Ci) Negative signs Optimization (O) Mania Hierarchical bottom-up insufficiency (Hbu) Avolition Connectivity Plasticity Constraint frustration (CF) Anxiety Hierarchical top-down shift (Htd) Delusions Hallucinations DNM Stimulus bound CF(b) Phobia
57 Environment Default Mode Network
58 Default Mode Network Environment Personality
59 Cs Ci Htd Hbu D O CF CF(b) DMN Clinical Brain Profiling (CBP) DNM
60 Brain Profiler
61 Bain Profiler All rights resaved August 2017 Brain Related Psychiatric Diagnosis
62 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 Cs connectivity segregation D De-optimization Ci connectivity integration Htd Hierarchal top down shift Clinical Brain Profiling (CBP) O Hyperoptimization CF Constrain Frustration Hbu Hierarchal Bottom-up insufficiency Disturbance to connectivity Optimal brain Disturbance to plasticity and Optimization CF(b) Constrain Frustration Bound Default Mode Network
63 The Clinical Brain Profile (CBP) of the patient looks like this: Millisecond range activity Connectivity Plasticity over weeks and months Plasticity Lifetime Developmental DNM Htd Hierarchical top-down shift Hbu Hierarchical bottom-up insufficiency Ci Connectivity integration Cs Connectivity segregation O Optimization D De- Optimization CF Constraint frustration CF(b) Stimulus bound DMN Default Mode Network Clinical Brain Profiling
64 Unsupervised fuzzy clustering of CBP into two categories classifies patients based on clinical severity validating CBP sensitivity to clinically meaningful phenomenology.
65 Archetype schizophrenia positive signs Archetype schizophrenia Negatives signs Corr = 0.56 Corr = Schizophrenia N= 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 Schizoaffective N=34 Archetype Schizoaffective Corr = Depression N=59 Archetype Depression Corr = Archetype Anxiety Anxiety N= Corr = Archetype Personality disorder Corr = 0.9 Personality Disorders N=40 Others N=
66 The Brain Profiler Site
67 Brain Profiler is a Computer and an App platform Brain Profiler All rights resaved August 2017
68 EEG Psychiatrist Assessment Objective digital Subjective Condition Powerful Statistics
69 Clinician Scale pheno CBP COMPUTATIONAL PSYCHIATRY Unique Patient Symptoms CBP Unique DMN CF(b) CF CBP O D Hbu Htd Ci Cs Cyber Mob sensors Obj digital CBP Clinical Brain Profiling (CBP) Brain Imaging VALID CBP Unique EMR Personalized Tele-medicine Alerts & Notifications Queuing and Management Treatment-Response
70 Signal Processing & Validation
71 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
72 Calculating correlation matrices can inform us about brain connectivity by constructing Graphs of connectivity patterns. These can be evaluated over different and extended timescales.
73 Graphs can help estimate Small-World optimal brain networks as well as fixed overly connected or randomly disconnected networks. SMALL WORLD NETWORK
74 Node attacks (random versus degree-related) can measure network reliance and vulnerability to functional perturbations Brain Profiler All rights resaved August 2017
75 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
76 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
77 Contact information Brain Profiler All rights resaved August 2017
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