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Leveraging The Finite States of Emotion Processing to Study Late-Life Mental Health

Yuanzhe Huang, Saurab Faruque, Minjie Wu, Akiko Mizuno, Eduardo Diniz, Shaolin Yang, George Dewitt Stetten, Noah Schweitzer, Hecheng Jin, Linghai Wang, Howard J. Aizenstein

Abstract

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression.

Leveraging The Finite States of Emotion Processing to Study Late-Life Mental Health

Abstract

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression.
Paper Structure (18 sections, 8 figures, 2 tables)

This paper contains 18 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. cvHMM
  3. cvHMM for fMRI
  4. cvHMM for Questionnaires
  5. Methods
  6. Data
  7. fMRI Data
  8. Questionnaire data and sample
  9. Mental health measures
  10. Results
  11. fMRI Data
  12. Questionnaire Data
  13. Questionnaire change-vectors
  14. Change-vectors and change-vector-patterns
  15. K-means induced change-states
  16. ...and 3 more sections

Figures (8)

  • Figure 1: The mean of bilateral amygdala, Viterbi, and K-Means that use all 115 scans data
  • Figure 2: 3-dimensional representation of k-means induced change-states, excluding exercise variable
  • Figure 3: Change-states defined by salient factor (i.e., its prominent contributory change-vector dimension)
  • Figure 4: Frequencies of change-state transitions for female subjects in k-means induced sequences.
  • Figure 5: Chi-squared residuals for male and female state transition frequencies in K-means induced change-state sequences.
  • ...and 3 more figures