One of the most common approaches, where the time series data is segmented into overlapping or non-overlapping windows.
Functional connectivity is calculated separately for each window using measures like Pearson correlation or partial correlation.
Provides insights into the temporal dynamics of connectivity patterns.
Dynamic Conditional Correlation Models:
Extends traditional correlation analysis by allowing the correlation coefficient to vary over time.
Models the temporal evolution of connectivity using dynamic linear models or time-varying autoregressive models.
Captures changes in connectivity strength and directionality across different time points.
Dynamic Graphical Models:
Represent brain networks as graphs where nodes correspond to brain regions and edges represent functional connections.
Employ dynamic graphical models such as dynamic Bayesian networks or dynamic graphical LASSO to estimate time-varying connectivity patterns.
Allows for the identification of evolving network structures and connectivity dynamics.
Hidden Markov Models (HMM):
Model the brain’s dynamic connectivity states as a sequence of hidden states governed by transition probabilities.
Estimate the most likely sequence of hidden states given the observed fMRI data using the Viterbi algorithm or forward-backward algorithm.
Provides a framework for characterizing discrete brain states and transitions between them.
Dynamic Connectivity Regression:
Incorporates external variables or experimental conditions as regressors to investigate how they modulate dynamic connectivity patterns.
Fit a regression model to estimate the relationship between covariates and time-varying connectivity metrics.
Allows for the investigation of task-evoked or stimulus-dependent changes in functional connectivity.
Time-Frequency Analysis:
Decompose the fMRI time series data into different frequency bands using techniques like wavelet transform or Hilbert-Huang transform.
Estimate dynamic connectivity within each frequency band to capture oscillatory dynamics of brain networks.
Reveals frequency-specific changes in connectivity patterns over time.
Functional Network Dynamics Analysis:
Characterize the dynamics of functional brain networks using graph-theoretical measures such as modularity, network flexibility, or resilience.
Investigate how network properties evolve over time and their relationship to cognitive or behavioral states.
Provides insights into the reconfiguration of functional brain networks during different tasks or mental states.
Deep Learning Approaches:
Utilize deep learning architectures such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs) to model the temporal dynamics of functional connectivity directly from fMRI data.
Train neural networks to predict future connectivity patterns based on past observations, enabling the capture of complex temporal dependencies.