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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Generative dynamical models for classification of rsfMRI data.

Grace Huckins¹, Russell A Poldrack²

¹Neurosciences Interdepartmental Program, Stanford University, Stanford, CA, USA.

Network Neuroscience (Cambridge, Mass.)

|December 30, 2024

Summary

This summary is machine-generated.

This study introduces a novel dynamical approach using hidden Markov models for classifying resting-state fMRI data. This method effectively leverages brain dynamics for within-subject classification, offering potential for greater interpretability.

Keywords:

Classification Generative models Hidden Markov models Network dynamics Resting-state fMRI

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Area of Science:

Neuroimaging
Machine Learning
Computational Neuroscience

Background:

Large-scale neuroimaging datasets and machine learning tools are increasingly used to predict psychological/behavioral variables from fMRI data.
Most studies classify fMRI data using static features, with fewer exploring brain dynamics for classification.

Purpose of the Study:

To pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data.
To leverage dynamical patterns in rsfMRI data for classification using hidden Markov models (HMMs).

Main Methods:

Fitting separate HMMs to classes in training data.
Classifying test data based on likelihood under trained HMMs.
Utilizing transition probabilities among hidden states for classification.

Main Results:

HMMs successfully performed within-subject classification on the MyConnectome dataset using transition probabilities.
Individual subject identification was not achieved using HMM transition probabilities alone on the Human Connectome Project dataset.
A vector autoregressive model achieved high performance for subject identification.

Conclusions:

The piloted dynamical classification approach for rsfMRI data shows promising performance, especially for within-subject classification.
This HMM-based approach has the potential to offer greater interpretability compared to other dynamical methods.
Further research is warranted to explore the full potential of dynamical models in neuroimaging analysis.