luciano phddefense

Neural Network Modeling of Unipolar Depression:Patterns of Recovery and Prediction of Outcome

Joanne Sylvia Luciano, Jr. B.S., M.S.

Data from: Depression Research Facility, McLean Hospital; Massachusetts Mental Health Center;

and Harvard Medical School

Dissertation Defense30 August 1995 5:15 PM

2 Cummington Street, Room 101Boston, MA 02215

Department of Cognitive and Neural Systems, Boston University

J. S. Luciano Ph.D. Defense

30 August 1995

Depression is a BIG problemCharacterized by persistent and pathological

sadness, dejection, and melancholyPrevalence (US)

17% experience it in lifetime10% a year (25 million)

Cost (US)$44 billion a year (1990)

Impact (US)1% improvement means 250,000 people helped1% means $440 million savings


30 August 1995

The Economic Burden of DepressionDepression Costs the U.S. $43.7 Billion Annually

Source: Paul Greenberg et alMIT Sloan School of Management/Analysis Group, Inc.

$23.8 Billion

$12.4 Billion

$7.5 Billion

Direct Costs:Treatment &

Rehabilitation

Loss of Earnings Due toDepression-Induced

Suicides

Workplace Costs:Absenteeism

& Lost Productivity


30 August 1995

Research Goals

Correct Treatment

IlluminatePath to Recovery

Individualized Treatment


30 August 1995

!"#$%&'("#)!*+)#!"#!"$%&"'%("

!"#$$$%&

)"*+,!-.

)"*+,!-/

,+,-./#(!,'0!#0!$%1#2%$,!


30 August 1995

Depression Background

Clinical DepressionTreatmentMeasurementNot specific diagnosisNot specific treatment


30 August 1995

Clinical Data

Hamilton Depression Rating Scale21 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

Hamilton Psychiatric Scale for Depression

1. DEPRESSED MOOD (Sadness, hopeless, helpless, worthless)

0 = Absent1 = These feeling states indicated only on questioning2 = These feeling states spontaneously reported verbally3 = Communicates feeling states non-verbally - i.e., through facial expression, posture, voice, and tendancy to weep4 = Patient reports VIRTUALLY ONLY these feeling states in his spontaneous verbal and non-verbal communication


30 August 1995

Modeling Background

Recast problem into mathematical terms

Easier to understandEasier to manipulateEasier to analyze


30 August 1995

TreatmentNot

DepressedDepressed

symptoms pattern outcome

ModelingRecovery

Predicting Response

Clinical Data


30 August 1995

Study # 1

Analyze path of RECOVERY


30 August 1995

Take Home MessagesA neural network model is capable of predicting and describing recovery patterns in depression.

Recovery patterns differ by treatmentCognitive Behavioral Therapy

is sequentialDesipramine

is concurrent (after delay)


30 August 1995

Understanding Recovery

Patient Recovery pattern (Differential Equations) x

TreatmentNot

DepressedDepressed

Compare patterns of recovery

Recast as dynamical system

6 week When response begins (Latency) ! ! t7 symptoms Indirect (between symptoms) (Interaction Effects) w2 treatments Direct (on symptoms) (Treatment Effects) u,v


30 August 1995

DepressionPhysical: E Sleep

M, L SleepEnergy

Performance: WorkPsychological: Mood

CognitionsAnxiety

7 Symptoms

2 Treatments Cognitive Behavioral Therapy (CBT)Desipramine (DMI)

Clinical Data Responders = improvement >= 50% N = 6 patients each study6 weeks = 252 data points each study


30 August 1995

OverviewRecovery Model and Parameters

Latency

TreatmentEffects

InteractionEffects

Treatment

wij

! t! t

vu , ii

=


30 August 1995

Modeling Time to Response

" Rapidness of response! t Latency

Latency

Treatment onset

!tLatency

h t te t t( , ) ( )""

# =+ # #

!!

11 !t

! t

iu ivsymptom


30 August 1995

Recovery Model Architecture

InteractionsAnxiety

Symptoms

w

Latency!tTreatment

! t

xi xj

vjviiu uj

wij

wji

Mood

Treatment Effectsu immediatev delayed

2 Models CBT DMI

x

Optimized parameters specify modelInitial conditions predict patient trajectory


30 August 1995

Recovery Model

x x

Treatment Effectson each symptom(strength)

Stabilizing factor

!! !

!, # t

Symptom

( )wx B ijj j#

Baseline

Interactions between symptoms

7 symptoms

Rate of symptom changeAi i i= #

+$j=1

7Acceleration of symptom

s(t) ui+

Latency

+ ( ) vih t

Immediate effectstep function

Delayed effect sigmoid function "

Steepness


30 August 1995

Training the Model

actual

estimated

Obtain optimized parameters-fit patient data -train on time course-minimize error term L-gradient descent on parameters

L = Error termX = datai = symptomsj = parameterk = patients

( ) ( )L X X X f X dt K Pik ik ik ik ikik

T

jj= # + #$% + $&'(

)*+

&'(

)*+" "! "

2

0

2µ


30 August 1995

Recovery Pattern and ErrorExample Patient (CBT)

0 10 20 30 400.20.40.60.8

Pat 1840201 ANXITEY [DAYS]0 10 20 30 40

0

0.2

Pat 1840201 COGNITIVE [DAYS]

0 10 20 30 400.20.4

Pat 1840201 MOOD [DAYS]0 10 20 30 40

0.40.60.8

Pat 1840201 WORK [DAYS]

0 10 20 30 400

0.5

Pat 1840201 ENERGY [DAYS]0 10 20 30 40

0

0.05

0.1

Pat 1840201 E SLEEP [DAYS]

0 10 20 30 400

0.05

0.1

Pat 1840201 M,L SLEEP [DAYS]0 200 400 600

0

100

200

(L=25.8) ERROR TREND [CYCLES]


30 August 1995

ResultsOptimized parameters specify model

Initial conditions predict pattern trajectory

Latency

TreatmentEffects

InteractionEffects

Treatment

wij

! t! t

viui 2 Models

CBT DMI


30 August 1995

Latency!!t = response delay

CBT: 1.2 weeks DMI: 3.4 weeks

0 1 2 3 4 5 6

Weeks!!t Latency parameter

CBT

DMI


30 August 1995

Mean 1/2 Reduction Time

CBT

DMI

0 1 2 3 4 5 6

2.57

3.74

1.51

3.54

1.37

2.09

2.21

2.67

2.76

2.1

4.63

2.96

5.04

3.89

AnxietyCognitionsMoodWorkEnergyE SleepM,L Sleep

Weeks

CBT varies 3.7DMI varies 1.8


30 August 1995

Direct Effect of TreatmentDesipramine

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Immediate 3.4 Weeks

Cognitive Behavioral Therapy

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Immediate 1.2 Weeks

AnxietyCognitionsMoodWorkEnergyE SleepM, L Sleep

(u )iImmediate vs. Delayed (v )icoefficients (strength)


30 August 1995

Direct Treatment Intervention Effect

CBT

! t

CM

MS

ES

W E

ui

TreatmentEffects

(u )i DMI

! t

A

CM

MS

ES

W E

v )i(u ,i (v )i

A AnxietyC CognitionsM MoodW Work

Weak

Strong

E EnergyES E SleepMS M, L Sleep

Immediate Delayed

A

iv,J. S. Luciano Ph.D. Defense

30 August 1995

Treatment Effects and Interactions DMI (delayed)

CONCURRENTCBT

SEQUENTIAL


30 August 1995

ConclusionsAn neural network model is capable of predicting and describing recovery patterns in depression.

Recovery patterns differ by treatmentCognitive Behavioral Therapy

is sequentialDesipramine

is concurrent (after delay)

Combined treatment for suicidal patents? Reduce suicidal tendency quickly?


30 August 1995

LimitationsModel:

Assumes symptoms interactAssumes treatment acts directlyPermanent vs. transientCausal vs. sequentialStatistical fluctuations not handled

Study:CBT measurement intervals varySmall sample sizeInitial 6 weeks of CBT (entire=16)Finer resolution of measurements (daily)


30 August 1995

Consistent with earlier studies

Bowden 1993 Mood at 3 weeks predicts response for fluoxetine (Prozac)

*some discrepancies with our patient data

Persistent improvement after delayQuitkin, 1984, 1987

Katz, 1987Mood and cognitive impairment at 1 week predicts response Retardation improves much later*

Nagayama 1991 Severity at 1 week predicts response


30 August 1995

Future Studies

Larger databaseNon-respondersOther illnessesOther measuresOther treatmentsLink to brain regions


30 August 1995

Study # 2

Predict Response to Treatment


30 August 1995

Will an individual respond?Methods

BackpropagationMultiple Regression

Output

7 SmptomsTreatmentSeverity99 patients

InputNone (Raw)NormalExponentialGamma

Transformations

Tells what to prescribeBetter match (diagnosis & treatment)1% = 250,000 people helped

$440 million saved

Categorical Yes/No

ContinuousHow much

Potential Benefits:


30 August 1995

16 Studies attempted to predict response

Prior Results

These comprise 224 individual findings:

Severity: 10 significant 9 not significant

Used linear methodsYielded inconsistent results

Symptoms 10 significant 95 not significant


30 August 1995

The Nonlinear Approach

Previous failures used linear methods, so....

We tried nonlinear methods

More powerfulCapture nonlinear interactions


30 August 1995

Data sets & PreprocessingInitial Input-Output Pairs

Categorical Continuous (normalize [0..1])

Raw

Exponential

Gamma

Normal

3 different models BP (2) BP (8) Linear Regression (MR)

z-score


30 August 1995

HOW?

Start simpleBackpropagation

Independent variables21 Symptoms, Severity, Treatment

Dependent variablesCategorical (Responder / Nonresponder)Continuous (percent improved)

Train on 99 Patients' data (symptoms, severity, treatment, response)


30 August 1995

MethodsData comprises 99 input/output pairs

Inputs21 Symptoms1 Overall Severity3 Treatments

OutputResponsea. Categorical (Yes/No)b. Continuous (% change)

PreprocessingRemove Irregularities (transformations: exp, gamma, norm)

Normalize (z-score [0..1])

Linear Regression, BP (2), BP (8) yields 24 datasets


30 August 1995

Results

Categorical (yes/no)

Best performance CorrectBackpropagation (2) Hidden Units 54.5 %Backpropagation (8) Hidden Units 54.5 %Linear Regression 51.5%Chance 50.0 %


30 August 1995

ResultsBest performance: Lowest RMS Error (%)

Raw Norm Exp GamMR 27.7 27.4 24.6 24.7BP (2 Hidden) 23.5 23.1 20.3 20.6Difference 3.2 6.1 4.3 5.1

Exponential transformation bestBP better than MR in every caseworst BP better than best MR


30 August 1995

Backpropagation (Nonlinear) Performed Slightly Better

BUT...

Still not statistically significant:

WHY?


30 August 1995

PerformanceTraining good, test poorNetwork can't generalize

# parameters 54 # samples 66

PoV should be 84%, but actually only 4.5%

Reduce 21 HDRS to 7 Symptom Factors

= = 84%Expected PoV =


30 August 1995

Reduced Dimensionality Results

Better, but...

still not statistically significant(F=0.0143, p=1.000)

Exponential Continuous RMS PoV21 items 20.3% 4.54%7 scores 19.9% 7.16%


30 August 1995

Is Data Random?

#params 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%

Expected PoV = 27%==


30 August 1995

Weight Analysis

Responder predicted when:

Fluoxetine (Prozac) (+)Cognitions (-)Early Sleep Disturbance (+)Anxiety (+)Severity (-)

(+) present (-) absent


30 August 1995

ConclusionsBP (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 variancefor the network is df/N = 27/99=27.3% for ramdom data Actual was 66.7% >> 27.3%

Suggestspredictive relationships are presentlarger study with more data needed


30 August 1995

SummaryNeural network methods applied to clinical research in depression

Useful to understanding recovery dynamics

More powerful than current methodsused for clinical depression research


30 August 1995

Future....Integrated Model

LinkSymptomsBrain Region ActivityNeurotransmitters

Combine data fromClinical StudiesAnimal ModelsImaging DataMetabolite Studies


30 August 1995

Link to the FutureIntegrate knowledge about:symptoms, brain regions, transmitter systems,pharmacological agents, and dynamicsto build integrated models

Two norepinephrine pathways locus coeruleus to the hypothalamusaffect feeding behavior.

One excites, the other inhibits.DMI (presynaptic drug) induces eating prevents norepinephrine inactivation by blocking reuptake

symptom: appetite & weight

Pontine (ACh)

++ - -

--

"-receptors

,-receptors

PeriventricularNucleus

PerifornicalNucleus

Pons

Hypothalamus

Locus Coeruleus

Stops Eating

Starts Eating


30 August 1995

luciano phddefense

Documents

t rapidness of response

t latencytreatment onset30

t visteepness latency30

recovery patterns

symptoms scale

feeling states

defenseoverview recovery

defense modeling time