機械学習
State-Space Modeling for fMRI Data
State-space models (SSMs) for fMRI analysis: HMM, HMM-MAR, sticky/HDP-HMM, IO-HMM, SLDS, rSLDS, SNLDS. Covers resting-state, task-based (MID, SST, N-back), and naturalistic fMRI (movie, gaming). Python code generation (hmmlearn, ssm, pyhsmm, osl-dynamics, glhmm), HRF-aware modeling, fMRIPrep/XCP-D preprocessing, CIFTI/parcellation/ICA, model selection, and single-subject + group-level inference. Trigger keywords: HMM on brain data, brain state dynamics, dynamic FC, switching dynamics, latent states from BOLD, HRF deconvolution for state models, SLDS/rSLDS on neural timeseries, choosing K for fMRI, state-space + neuroimaging, task paradigms (MID, SST, N-back, movie-watching) with dynamic/latent-state analysis, temporal dynamics beyond standard GLM.