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

• URL: http://arxiv.org/abs/2403.03414v1
• Date: Wed, 6 Mar 2024 02:46:17 GMT
• Title: Leveraging The Finite States of Emotion Processing to Study Late-Life Mental Health
• Authors: 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 summary: Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs. 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.
• Score: 0.3370543514515051
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems [9.29069202652354]
  An Explainable AI for Quantitative analysis (QXAI) framework is proposed with post-hoc model explainability and intrinsic explainability for regression and classification tasks. We adopted the artificial neural networks (ANN) and attention-based Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and classification of physical activities based on sensor data.
  arXiv  Detail & Related papers  (2023-09-19T03:50:30Z)
• Physics Symbolic Learner for Discovering Ground-Motion Models Via NGA-West2 Database [4.059252581613122]
  Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method is proposed to automatically discover mathematical equation operators as symbols.
  arXiv  Detail & Related papers  (2023-03-23T04:14:05Z)
• Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics [64.72260320446158]
  We propose a method for synthesising controllers for Markov jump linear systems. Our method is based on a finite-state abstraction that captures both the discrete (mode-jumping) and continuous (stochastic linear) behaviour of the MJLS. We apply our method to multiple realistic benchmark problems, in particular, a temperature control and an aerial vehicle delivery problem.
  arXiv  Detail & Related papers  (2022-12-01T17:36:30Z)
• Combining Machine Learning and Agent-Based Modeling to Study Biomedical Systems [0.0]
  Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn from data on their own, without imposing a priori theories of system behavior.
  arXiv  Detail & Related papers  (2022-06-02T15:19:09Z)
• DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals [62.997667081978825]
  We develop a novel statistical point process model-called driven temporal point processes (DriPP) We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses.
  arXiv  Detail & Related papers  (2021-12-08T13:07:21Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Autoregressive Hidden Markov Models with partial knowledge on latent space applied to aero-engines prognostics [2.179313476241343]
  This paper describes an Autoregressive Partially-hidden Markov model (ARPHMM) for fault detection and prognostics of equipments based on sensors' data. We show how to apply this model to estimate the remaining useful life based on health indicators.
  arXiv  Detail & Related papers  (2021-05-01T10:23:22Z)
• Identification of brain states, transitions, and communities using functional MRI [0.5872014229110214]
  We propose a Bayesian model-based characterization of latent brain states and showcase a novel method based on posterior predictive discrepancy. Our results obtained through an analysis of task-fMRI data show appropriate lags between external task demands and change-points between brain states.
  arXiv  Detail & Related papers  (2021-01-26T08:10:00Z)
• Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach [144.21892195917758]
  We study estimation in a class of generalized Structural equation models (SEMs) We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using a gradient descent. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.
  arXiv  Detail & Related papers  (2020-07-02T17:55:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.