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Joanne S. Luciano, PhD Defense @ Boston University, 1996. Neural Network Models of Unipolar Depression. Patterns of Recovery and Prediction of Outcome. Work lead to two US Patents

Joanne S. Luciano, PhD Defense @ Boston University, 1996. Neural Network Models of Unipolar Depression. Patterns of Recovery and Prediction of Outcome. Work lead to two US Patents

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    • J. S. Luciano Ph.D. Defense Neural Network Modeling of Unipolar Depression: Patterns of Recovery and Prediction of Outcome Joanne Sylvia Luciano, Jr. B.S., M.S. Dissertation Defense 30 August 1995 5:15 PM 2 Cummington Street, Room 101 Boston, MA 02215 Department of Cognitive and Neural Systems, Boston University Data from:Depression Research Facility, McLean Hospital; Massachusetts Mental Health Center; and Harvard Medical School30 August 1995 Page 1
    • J. S. Luciano Ph.D. Defense Depression is a BIG problem Characterized by persistent and pathological sadness, dejection, and melancholy Prevalence (US) 17% experience it in lifetime 10% a year (25 million) Cost (US) $44 billion a year (1990) Impact (US) 1% improvement means 250,000 people helped 1% means $440 million savings30 August 1995 Page 2
    • J. S. Luciano Ph.D. Defense The Economic Burden of Depression Depression Costs the U.S. $43.7 Billion Annually Workplace Costs: Absenteeism Direct Costs: & Lost Productivity Treatment & Rehabilitation $23.8 Billion $12.4 Billion $7.5 Billion Loss of Earnings Due to Depression-Induced Suicides Source: Paul Greenberg et al MIT Sloan School of Management/Analysis Group, Inc.30 August 1995 Page 3
    • J. S. Luciano Ph.D. Defense Research Goals Illuminate Path to Recovery Correct Treatment Individualized Treatment30 August 1995 Page 4
    • J. S. Luciano Ph.D. Defense Study #2 predict response to treatment Study #1 analyze path of recovery YES NO30 August 1995 Page 5
    • J. S. Luciano Ph.D. Defense Depression Background Clinical Depression Treatment Measurement Not specific diagnosis Not specific treatment30 August 1995 Page 6
    • J. S. Luciano Ph.D. Defense Clinical Data Hamilton Depression Rating Scale 21 Symptoms (scale of 0..4) Overall Severity of Depression Treatments (3 clinical studies) Desipramine (DMI) Cognitive Behavioral Therapy (CBT) Fluoxetine (Prozac) Outcome (responded to treatment) Categorical (YES/NO) Continuous (How much? % change)30 August 1995 Page 7
    • J. S. Luciano Ph.D. Defense Hamilton Psychiatric Scale for Depression 1. DEPRESSED MOOD (Sadness, hopeless, helpless, worthless) 0 = Absent 1 = These feeling states indicated only on questioning 2 = These feeling states spontaneously reported verbally 3 = Communicates feeling states non-verbally - i.e., through facial expression, posture, voice, and tendancy to weep 4 = Patient reports VIRTUALLY ONLY these feeling states in his spontaneous verbal and non-verbal communication30 August 1995 Page 8
    • J. S. Luciano Ph.D. Defense Modeling Background Recast problem into mathematical terms Easier to understand Easier to manipulate Easier to analyze30 August 1995 Page 9
    • J. S. Luciano Ph.D. Defense Treatment Depressed Not Depressed symptoms pattern outcome Clinical Modeling Predicting Data Recovery Response30 August 1995 Page 10
    • J. S. Luciano Ph.D. Defense Study # 1 Analyze path of RECOVERY30 August 1995 Page 11
    • J. S. Luciano Ph.D. Defense Take Home Messages A neural network model is capable of predicting and describing recovery patterns in depression. Recovery patterns differ by treatment Cognitive Behavioral Therapy is sequential Desipramine is concurrent (after delay)30 August 1995 Page 12
    • J. S. Luciano Ph.D. Defense Understanding Recovery Treatment Depressed Not Depressed Compare patterns of recovery 6 week When response begins (Latency) !t 7 symptoms Indirect (between symptoms) (Interaction Effects) w 2 treatments Direct (on symptoms) (Treatment Effects) u,v Recast as dynamical system Patient Recovery pattern (Differential Equations) x30 August 1995 Page 13
    • J. S. Luciano Ph.D. Defense Depression 7 Symptoms Physical: E Sleep M, L Sleep Energy Performance: Work Psychological: Mood Cognitions Anxiety 2 Treatments Cognitive Behavioral Therapy (CBT) Desipramine (DMI) Clinical Data Responders = improvement >= 50% N = 6 patients each study 6 weeks = 252 data points each study30 August 1995 Page 14
    • J. S. Luciano Ph.D. Defense Overview Recovery Model and Parameters Interaction Effects w ij Treatment Effects u ,v i i !t Latency !t = Treatment30 August 1995 Page 15
    • J. S. Luciano Ph.D. Defense Modeling Time to Response symptom Latency !t u v i i h (" , t # ! t ) = 1 Latency 1+ e#" (t#! t) !t " Rapidness of response !t ! t Latency Treatment onset30 August 1995 Page 16
    • J. S. Luciano Ph.D. Defense Recovery Model Architecture Symptoms wij x Mood Anxiety xi jx Interactions wji w Treatment ui uj vi vj Effects u immediate !t v delayed 2 Models CBT Latency DMI Treatment !t Optimized parameters specify model Initial conditions predict patient trajectory30 August 1995 Page 17
    • J. S. Luciano Ph.D. Defense Recovery Model Stabilizing factor i = # Ai xi x Rate of symptom change Interactions Acceleration of between symptoms symptom 7 Symptom Baseline 7 symptoms +$ ( x # B ) wij j j j =1 Immediate effect Treatment Effects step function + s(t) ui on each symptom (strength) Delayed effect sigmoid function + h ( ", t # ! t ) vi Steepness Latency30 August 1995 Page 18
    • J. S. Luciano Ph.D. Defense Training the Model estimated T & 2 &  ik # f Xik ) ) dt + K $ Pj2 0 (  L = % $ik ( Xik # Xik ) + µ ik ( X ( ) ++ ** j actual Obtain optimized parameters L = Error term X = data -fit patient data i = symptoms -train on time course j = parameter -minimize error term L k = patients -gradient descent on parameters30 August 1995 Page 19
    • J. S. Luciano Ph.D. Defense Recovery Pattern and Error Example Patient (CBT) 0.8 0.6 0.2 0.4 0.2 0 0 10 20 30 40 0 10 20 30 40 Pat 1840201 ANXITEY [DAYS] Pat 1840201 COGNITIVE [DAYS] 0.8 0.4 0.6 0.2 0.4 0 10 20 30 40 0 10 20 30 40 Pat 1840201 MOOD [DAYS] Pat 1840201 WORK [DAYS] 0.1 0.5 0.05 0 0 0 10 20 30 40 0 10 20 30 40 Pat 1840201 ENERGY [DAYS] Pat 1840201 E SLEEP [DAYS] 0.1 200 0.05 100 0 0 0 10 20 30 40 0 200 400 600 Pat 1840201 M,L SLEEP [DAYS] (L=25.8) ERROR TREND [CYCLES]30 August 1995 Page 20
    • J. S. Luciano Ph.D. Defense Results Optimized parameters specify model Initial conditions predict pattern trajectory Interaction Effects w ij Treatment Effects u v i i Latency 2 Models !t !t CBT DMI Treatment30 August 1995 Page 21
    • J. S. Luciano Ph.D. Defense Latency !t = response delay CBT: 1.2 weeks DMI: 3.4 weeks CBT DMI 0 1 2 3 4 5 6 Weeks !t Latency parameter30 August 1995 Page 22
    • J. S. Luciano Ph.D. Defense Mean 1/2 Reduction Time 2.57 Anxiety 1.51 Cognitions 1.37 CBT 2.21 Mood 2.76 Work 4.63 5.04 Energy E Sleep M,L Sleep 3.74 3.54 2.09 DMI 2.67 CBT varies 3.7 2.1 2.96 3.89 DMI varies 1.8 0 1 2 3 4 5 6 Weeks30 August 1995 Page 23
    • J. S. Luciano Ph.D. Defense Direct Effect of Treatment Cognitive Behavioral Therapy Desipramine Anxiety Cognitions Mood Work Energy E Sleep M, L Sleep 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Immediate 1.2 Weeks Immediate 3.4 Weeks coefficients (strength) Immediate (ui ) vs. Delayed (v i )30 August 1995 Page 24
    • J. S. Luciano Ph.D. Defense Direct Treatment Intervention Effect ui , vi W W M E M E C ES C ES A MS A MS !t Treatment !t Effects Immediate (u i ,vi ) Delayed (u i ) CBT DMI (v i ) A Anxiety E Energy C Cognitions ES E Sleep Weak M Mood MS M, L Sleep Strong W Work30 August 1995 Page 25
    • J. S. Luciano Ph.D. Defense Treatment Effects and Interactions CBT DMI (delayed) SEQUENTIAL CONCURRENT30 August 1995 Page 26
    • J. S. Luciano Ph.D. Defense Conclusions An neural network model is capable of predicting and describing recovery patterns in depression. Recovery patterns differ by treatment Cognitive Behavioral Therapy is sequential Desipramine is concurrent (after delay) Combined treatment for suicidal patents? Reduce suicidal tendency quickly?30 August 1995 Page 27
    • J. S. Luciano Ph.D. Defense Limitations Model: Assumes symptoms interact Assumes treatment acts directly Permanent vs. transient Causal vs. sequential Statistical fluctuations not handled Study: CBT measurement intervals vary Small sample size Initial 6 weeks of CBT (entire=16) Finer resolution of measurements (daily)30 August 1995 Page 28
    • J. S. Luciano Ph.D. Defense Consistent with earlier studies Quitkin, 1984, 1987 Persistent improvement after delay Katz, 1987 Mood and cognitive impairment at 1 week predicts response Retardation improves much later* Nagayama 1991 Severity at 1 week predicts response Bowden 1993 Mood at 3 weeks predicts response for fluoxetine (Prozac) *some discrepancies with our patient data30 August 1995 Page 29
    • J. S. Luciano Ph.D. Defense Future Studies Larger database Non-responders Other illnesses Other measures Other treatments Link to brain regions30 August 1995 Page 30
    • J. S. Luciano Ph.D. Defense Study # 2 Predict Response to Treatment30 August 1995 Page 31
    • J. S. Luciano Ph.D. Defense Will an individual respond? Input Transformations 7 Smptoms None (Raw) Methods Treatment Normal Backpropagation Severity Exponential Multiple Regression 99 patients Gamma Potential Benefits: Output Tells what to prescribe Categorical Better match (diagnosis & treatment) Yes/No Continuous 1% = 250,000 people helped How much $440 million saved30 August 1995 Page 32
    • J. S. Luciano Ph.D. Defense Prior Results Used linear methods Yielded inconsistent results 16 Studies attempted to predict response These comprise 224 individual findings: Severity: Symptoms 10 significant 10 significant 9 not significant 95 not significant30 August 1995 Page 33
    • J. S. Luciano Ph.D. Defense The Nonlinear Approach Previous failures used linear methods, so.... We tried nonlinear methods More powerful Capture nonlinear interactions30 August 1995 Page 34
    • J. S. Luciano Ph.D. Defense Data sets & Preprocessing Initial Input-Output Pairs Categorical Continuous (normalize [0..1]) Raw Exponential Gamma Normal z-score 3 different models BP (2) BP (8) Linear Regression (MR)30 August 1995 Page 35
    • J. S. Luciano Ph.D. Defense HOW?Start simple BackpropagationIndependent variables 21 Symptoms, Severity, TreatmentDependent variables Categorical (Responder / Nonresponder) Continuous (percent improved)Train on 99 Patients data (symptoms, severity, treatment, response)30 August 1995 Page 36
    • J. S. Luciano Ph.D. Defense Methods Data comprises 99 input/output pairs Inputs Output 21 Symptoms Response 1 Overall Severity a. Categorical (Yes/No) 3 Treatments b. Continuous (% change) Preprocessing Remove Irregularities (transformations: exp, gamma, norm) Normalize (z-score [0..1]) Linear Regression, BP (2), BP (8) yields 24 datasets30 August 1995 Page 37
    • J. S. Luciano Ph.D. Defense Results Categorical (yes/no) Best performance Correct Backpropagation (2) Hidden Units 54.5 % Backpropagation (8) Hidden Units 54.5 % Linear Regression 51.5% Chance 50.0 %30 August 1995 Page 38
    • J. S. Luciano Ph.D. Defense Results Best performance: Lowest RMS Error (%) Raw Norm Exp Gam MR 27.7 27.4 24.6 24.7 BP (2 Hidden) 23.5 23.1 20.3 20.6 Difference 3.2 6.1 4.3 5.1 Exponential transformation best BP better than MR in every case worst BP better than best MR30 August 1995 Page 39
    • J. S. Luciano Ph.D. Defense Backpropagation (Nonlinear) Performed Slightly Better BUT... Still not statistically significant: WHY?30 August 1995 Page 40
    • J. S. Luciano Ph.D. Defense Performance Training good, test poor Network cant generalize # parameters 54 Expected PoV = # samples = 66 = 84% PoV should be 84%, but actually only 4.5% Reduce 21 HDRS to 7 Symptom Factors30 August 1995 Page 41
    • J. S. Luciano Ph.D. Defense Reduced Dimensionality Results Better, but... Exponential Continuous RMS PoV 21 items 20.3% 4.54% 7 scores 19.9% 7.16% still not statistically significant (F=0.0143, p=1.000)30 August 1995 Page 42
    • J. S. Luciano Ph.D. Defense Is Data Random? #params 27 Expected PoV = = = 27% #samples 99 Trial PoV Chance (expected) 27.27 % Chance (random sample) 24.85 % Raw Categorical 46.46 % Exp Categorical 45.87 % Raw Continuous 45.33 % Exp Continuous 66.61%30 August 1995 Page 43
    • J. S. Luciano Ph.D. Defense Weight Analysis Responder predicted when: Fluoxetine (Prozac) (+) Cognitions (-) Early Sleep Disturbance (+) Anxiety (+) Severity (-) (+) present (-) absent30 August 1995 Page 44
    • J. S. Luciano Ph.D. Defense Conclusions BP (nonlinear method) consistently outperformed MR (linear method) Still, the prediction was not statistically significant (F=0.0143, p=1.000) But, the theory implies proportion of variance for the network is df/N = 27/99=27.3% for ramdom data Actual was 66.7% >> 27.3% Suggests predictive relationships are present larger study with more data needed30 August 1995 Page 45
    • J. S. Luciano Ph.D. Defense Summary Neural network methods applied to clinical research in depression Useful to understanding recovery dynamics More powerful than current methods used for clinical depression research30 August 1995 Page 46
    • J. S. Luciano Ph.D. Defense Future.... Integrated Model Link Symptoms Brain Region Activity Neurotransmitters Combine data from Clinical Studies Animal Models Imaging Data Metabolite Studies30 August 1995 Page 47
    • J. S. Luciano Ph.D. Defense Link to the Future Integrate knowledge about: symptoms, brain regions, transmitter systems, pharmacological agents, and dynamics Hypothalamus to build integrated models Periventricular Starts Eating Nucleus symptom: appetite & weight Locus Two norepinephrine pathways Pons Coeruleus - locus coeruleus to the hypothalamus + - affect feeding behavior. + "-receptors One excites, the other inhibits. ,-receptors - DMI (presynaptic drug) induces eating Pontine (ACh) prevents norepinephrine inactivation - by blocking reuptake Stops Eating Perifornical Nucleus30 August 1995 Page 48