University of Texas Health Science Center at San Antonio DYNAMIC CO-VARIATION OF SYMPTOMS OF ANXIETY AND DEPRESSION AMONG NEWLY-DIAGNOSED PATIENTS WITH MAJOR DEPRESSIVE EPISODE, PANIC DISORDER, AND CONTROLS David Katerndahl Chen-Pin Wang University of Texas Health Science Center at San Antonio San Antonio, Texas

ANXIETY-DEPRESSION CORRELATIONS Symptom Levels Correlate Community-Dwelling Adults Adults Seeking Psychiatric Treatment Patients With: Hypertension Depression Symptom Variabilities Similar Posttraumatic Stress Disorder Controls Most Similar

DETERMINANTS OF DYNAMICS Attractors

Attractors An attractor is a point or a collection of points on which the system can limit. These often take the form of -Fixed Points -Periodic Orbits -Strange Attractors

Basins of Attraction The Basin of Attraction for an attractor is the set of points which limit on the attractor.

Basins of Attraction The set of all points in phase space that are under the influence of an attractor, or, more generally, the initial conditions of a system that evolve into the range of behavior allowed by the attractor. If one imagines a complex system as a sink, then the attractor can be considered the drain at the bottom, and the basin of attraction is the sink’s basin.

PURPOSE Explore The Relationship Between Dynamics Of Symptoms Of Anxiety And Depression Do Anxiety And Depression Co-Vary? Are There Diagnostic Differences In: Symptom Dynamics Bivariate Attractors

SAMPLE Setting Family Health Center Acute Care Clinic 5 Patients Per Diagnostic Group – Referred By PCP Panic Disorder Major Depression Neither Verification – SCID-IV Major Depressive Episode

PROCEDURE Baseline Information Demographic Information Chart Audit Active Diagnoses Prior Mental Health Treatments Treatment For Newly-Diagnosed Illnesses

PROCEDURE Mood Diary Procedure Hourly (While Awake) Assessments Of Levels Anxiety - “very relaxed” to “very nervous” Depression - “very happy” to “very sad” 100-mm Visual Analog Scales Symptoms Recoded 1-10 Procedure Enrollment In Clinic Wrist Watch With Hourly Chime As Reminder Weekly Telephone Encouragement Termination After 30 Days

COVARIATION ANALYSES Intrapatient Symptom Correlation dSEM Comparing Fit Statistics Predicting Acceleration Intergroup Comparison Z-Transformed Correlations – Kruskal-Wallis ANOVA (p ≤ .15) dSEM – Significance Of Diagnostic Terms (p ≤ .05)

Differential Structural Equation Modeling (DSEM) Extension Of SEM Models Derivatives Of Scores Velocity Acceleration Forces Equilibrium (Linearity) – Acceleration And Score Friction – Acceleration And Velocity Can Study Correlation Between Multiple Dynamic Systems

DIFFERENTIAL STRUCTURAL EQUATION MODELING Anxiety d Anxiety d2 Anxiety Depression d Depression d2 Depression

ATTRACTOR ANALYSES State Space Grid Identify Attractors Attractiveness Plot Anxiety State (1-10) versus Depression State (1-10) Identify Attractors Compute Heterogeneities (Cell Deviation From Randomness) Winnowing Using Scree Attractiveness Influence = Probability Outside Event Moves To Attractor Stability = Once Exited From Attractor, Time Until Returned Consistency = Recurrence Of Attractor Over Time Intergroup Comparisons – Kruskal-Wallis ANOVA Subjects (p ≤ .15) Attractors (p ≤ .05)

STATE SPACE GRID (example) Level Of Depression 1 2 3 4 5 6 7 8 9 10 2 4 1 1 1 Level Of Anxiety

CALCULATING HETEROGENEITY (Cell-Based Analysis) Identify Total Duration Of Time Total Number Of Unique Cells Visited Expected Duration Per Cell = Total Duration / Number Of Cells Order Cell Duration Times (From Least-To-Most) Record Number Of Cells With Each Duration Calculate Heterogeneity For Each Cell Duration Time Total Heterogeneity = Sum Of Cell Heterogeneities Mean Heterogeneity = Total Heterogeneity / Number Of Cells - If Initial Mean Heterogeneity < 1, No Attractor Present Eliminate Cells With Smallest Duration Re-Do Steps 1-4 Until Only Largest Duration Cell(s) Left Assessment For Each Step, Calculate The Proportional Mean Heterogeneity Against The Original Mean Heterogeneity Construct Scree Plot Identify Attractor Drop Of 50% Heterogeneity Greatest Drop In Heterogeneity Final Drop Eliminate Primary Attractor(s) And Recalculate Total Mean Heterogeneity - If > 1, Secondary Attractor Can Be Identified

IDENTIFYING ATTRACTORS

PRIMARY INITIAL ATTRACTORS Level Of Depression 1 2 3 4 5 6 7 8 9 10 1 4 3 1 Level Of Anxiety Control Depression Panic

ATTRACTOR CONSISTENCY (P04) Level Of Depression 1 2 3 4 5 6 7 8 9 10 Level Of Anxiety Week 1 Week 2 Week 3 Week 4

ATTRACTOR EVOLUTION (D03) Level Of Depression 1 2 3 4 5 6 7 8 9 10 Level Of Anxiety Week 1 Week 2 Week 3 Week 4

DEMOGRAPHICS OF SAMPLE Characteristic Controls (n=5) Subjects With MDE Subjects With PD Gender (#female) Ethnicity (Hispanic) Age (years) #<40 #≥40 Education # 0-11 Years # High School Grad #>12 Years Income #<$10,000 #$10,000-$30,000 Antidepressants Started Missing Data (Anx / Dep) Subject #1 Subject #2 Subject #3 Subject #4 3 5 4 1 2 0.0% 1.2% 0.4% 0.2% 0.0% 0.0% 2.4% 0.7% 0.0% 0.2% 40.1% 38.7% 0.8% 1.9% 0.6% 0.4%

INTERGROUP COMPARISON OF SYMPTOM EXTREMES AND CORRELATION Mean Daily Maximum Anxiety* Depression** Mean Daily Minimum Anxiety Depression Correlation r*** Controls Depression Panic 58 66 95 97 90 95 6 8 13 18 11 12 .70 .40 .44 * p ≤ .05 Controls < Depression, Panic ** p ≤ .05 Controls < Depression, Panic *** p ≤ .15 Controls > Depression, Panic

Depression Acceleration DSEM RESULTS Dependent Variable Depression Acceleration MODEL 1 MODEL 2 Anxiety Acceleration Fit Statistics -LogL AIC BIC Adjusted BIC 583 1179 1218 1199 253 524 582 554 898 1807 1846 1827 660 1338 1396 1367

DSEM RESULTS Characteristics Depression Acceleration Anxiety Estimate p-Value Anxiety Within Subjects Anxiety Level Anxiety Velocity Anxiety Acceleration Depression Level Depression Velocity Depression Acceleration Between Subjects Depression Dx Panic Dx 0.207 <.001 -0.083 .271 0.199 .028 -0.285 <.001 -0.047 .342 --- --- 0.112 .003 0.129 .002 -0.299 <.001 -0.153 .089 --- --- 0.266 <.001 0.165 .147 0.237 .028 -0.031 .134 -0.001 .960 TOTAL 0.224 .007 0.111 .051

INTERGROUP ATTRACTOR COMPARISONS (Subjects) Initial Cells Used **C (#) Initial Attractors Total Attractors*A Initial Attractor Consistency*B (# weeks) Duration On Attractors*D (% hours) Controls 14.0 1.3 1.8 2.0 64% Depression 26.3 5.0 0.0 41% Panic 3.0 34% * p ≤ .15 ** p ≤ .05 A = Depression > Panic, Control C = Control < Panic, Depression B = Depression < Panic, Control D = Control > Panic, Depression

INTERGROUP ATTRACTOR COMPARISONS (Attractors) Size**A (# cells) Proportional Duration**A (% time) Stability*B (return hours) Influence (% to attractor) Consistency*C (# weeks) Controls 2.6 50% 1.04 18% 1.8 Depression 2.0 36% 2.45 9% 0.4 Panic 23% 2.03 0.9 * p ≤ .05 ** p ≤ .01 A = Control > MDE, Panic B = Control < MDE, Panic C = Depression < Panic, Control

DISCUSSION Co-Variation Of Anxiety And Depression Significant Anxiety-Depression Correlation dSEM Fit Better With Both Symptoms In Model Presence Of Attractors

DISCUSSION Diagnostic Differences (Controls vs Illness) SymptomMAX & Correlations Differ dSEM – Both Disorders Contribute To d2 Depression Attractors Subjects (Controls) Fewer Initial Cells Used More Time On Attractors Attractors (Controls) Larger Attractors More Time On Attractor Quicker Return Time

DISCUSSION Diagnostic Differences (Depression vs Panic) No SymptomMAX & Correlation Differences dSEM – No Diagnostic Differences Attractors (Depression vs Panic) Subjects More Total Attractors Less Consistent Initial Attractors Attractors – Less Consistency

IMPLICATIONS Understanding Normalcy Emotional Variability Anxiety-Depression Interrelationship Psychopathology May Represent Weakening Of Attractors Perceived Instability Perceived Lack Of Control Diagnostic Differences Due To Dynamics Rather Than Levels? New Interventions To Strengthen Attractors

LIMITATIONS Small Sample Size Referral Of Subjects – Selection Bias? Missing Data Visual Analog Scales – Single Measurement Measures Taken Only While Awake Attractor-Level Comparisons Not Independent

CONCLUSIONS High Co-Variability Between Symptoms Dynamic Differences Seen Controls versus Patients Depression versus Panic Methodological Potential dSEM State Space Grid