Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.
OBJECTIVE
Most methods used in functional MRI (fMRI) brain mapping require restrictive assumptions about the shape and timing of the fMRI signal in activated voxels. Consequently, fMRI data may be partially and misleadingly characterized, leading to suboptimal or invalid inference. To limit these assumptions and to capture the broad range of possible activation patterns, a novel statistical fMRI brain mapping method is proposed. It relies on hidden semi-Markov event sequence models (HSMESMs), a special class of hidden Markov models (HMMs) dedicated to the modeling and analysis of event-based random processes.


METHODS
Activation detection is formulated in terms of time coupling between (1) the observed sequence of hemodynamic response onset (HRO) events detected in the voxel's fMRI signal and (2) the "hidden" sequence of task-induced neural activation onset (NAO) events underlying the HROs. Both event sequences are modeled within a single HSMESM. The resulting brain activation model is trained to automatically detect neural activity embedded in the input fMRI data set under analysis. The data sets considered in this article are threefold: synthetic epoch-related, real epoch-related (auditory lexical processing task), and real event-related (oddball detection task) fMRI data sets.


RESULTS
Synthetic data: Activation detection results demonstrate the superiority of the HSMESM mapping method with respect to a standard implementation of the statistical parametric mapping (SPM) approach. They are also very close, sometimes equivalent, to those obtained with an "ideal" implementation of SPM in which the activation patterns synthesized are reused for analysis. The HSMESM method appears clearly insensitive to timing variations of the hemodynamic response and exhibits low sensitivity to fluctuations of its shape (unsustained activation during task). Real epoch-related data: HSMESM activation detection results compete with those obtained with SPM, without requiring any prior definition of the expected activation patterns thanks to the unsupervised character of the HSMESM mapping approach. Along with activation maps, the method offers a wide range of additional fMRI analysis functionalities, including activation lag mapping, activation mode visualization, and hemodynamic response function analysis. Real event-related data: Activation detection results confirm and validate the overall strategy that consists in focusing the analysis on the transients, time-localized events that are the HROs.


CONCLUSIONS
All the experiments performed on synthetic and real fMRI data demonstrate the relevance of HSMESMs in fMRI brain mapping. In particular, the statistical character of these models, along with their learning and generalizing abilities are of particular interest when dealing with strong variabilities of the active fMRI signal across time, space, experiments, and subjects.
Faisan
Sylvain
S
Université Louis Pasteur, Strasbourg, France. faisan@ensps.u-strasbg.fr
Thoraval
Laurent
L
Armspach
Jean-Paul
JP
Foucher
Jack R
JR
Metz-Lutz
Marie-Noëlle
MN
Heitz
Fabrice
F
D000293:Adolescent; D000328:Adult; D016477:Artifacts; D001185:Artificial Intelligence; D001307:Auditory Perception / Q000502:physiology; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D005188:False Negative Reactions; D005189:False Positive Reactions; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D016571:Neural Networks, Computer; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors