Luciano phddefense

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 School
30 August 1995 Page 1


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 savings



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.



Research Goals

Illuminate
Path to Recovery

Correct Treatment

Individualized Treatment


Study #2

predict response to treatment

Study #1
analyze path of recovery YES NO



Depression Background

Clinical Depression
Treatment
Measurement
Not specific diagnosis
Not specific treatment



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)


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 communication



Modeling Background

Recast problem into mathematical terms

Easier to understand
Easier to manipulate
Easier to analyze



Treatment
Depressed Not
Depressed

symptoms pattern outcome

Clinical Modeling Predicting
Data Recovery Response


Study # 1

Analyze path of RECOVERY



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)


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) x


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 study



Overview
Recovery Model and Parameters
Interaction
Effects
w
ij
Treatment
Effects

u ,v
i i !t
Latency
!t
=
Treatment


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 onset


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 trajectory


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 Latency


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 parameters



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]


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
Treatment


Latency
!t = response delay
CBT: 1.2 weeks
DMI: 3.4 weeks

CBT

DMI
0 1 2 3 4 5 6

Weeks
!t Latency parameter


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
Weeks


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 )


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 Work


Treatment Effects and Interactions

CBT DMI (delayed)
SEQUENTIAL CONCURRENT



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?


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)


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 data


Future Studies
Larger database
Non-responders
Other illnesses
Other measures
Other treatments
Link to brain regions



Study # 2

Predict Response to Treatment



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 saved


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 significant



The Nonlinear Approach

Previous failures used linear methods, so....

We tried nonlinear methods

More powerful
Capture nonlinear interactions



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)


HOW?

Start simple
Backpropagation

Independent variables
21 Symptoms, Severity, Treatment

Dependent variables
Categorical (Responder / Nonresponder)
Continuous (percent improved)

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


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 datasets



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 %



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 MR


Backpropagation (Nonlinear)
Performed Slightly Better

BUT...

Still not statistically significant:

WHY?



Performance
Training good, test poor
Network can't generalize

# parameters 54
Expected PoV = # samples = 66
= 84%

PoV should be 84%, but actually only 4.5%

Reduce 21 HDRS to 7 Symptom Factors


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)


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%


Weight Analysis
Responder predicted when:

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

(+) present (-) absent



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 needed


Summary
Neural network methods applied to
clinical research in depression

Useful to understanding recovery dynamics

More powerful than current methods
used for clinical depression research



Future....
Integrated Model
Link
Symptoms
Brain Region Activity
Neurotransmitters
Combine data from
Clinical Studies
Animal Models
Imaging Data
Metabolite Studies


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
Nucleus

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