A practical model-based segmentation approach for improved activation detection in single-subject functional magnetic resonance imaging studies

• Published in: Human brain mapping 2023-11, Vol.44 (16), p.5309-5335
• Main Authors: Chen, Wei-Chen, Maitra, Ranjan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Brain research; Cerebrum; Computer Simulation; Datasets; Experiments; Functional magnetic resonance imaging; Humans; Image processing; Image segmentation; Imagination; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Methods; Neighborhoods; Patients; Persistent vegetative state; Traumatic brain injury
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26425

Functional magnetic resonance imaging (fMRI) maps cerebral activation in response to stimuli but this activation is often difficult to detect, especially in low-signal contexts and single-subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated and that these voxels are spatially localized, but it is challenging to incorporate both these facts. We address these twin challenges to single-subject and low-signal fMRI by developing a computationally feasible and methodologically sound model-based approach, implemented in the R package MixfMRI, that bounds the a priori expected proportion of activated voxels while also incorporating spatial context. An added benefit of our methodology is the ability to distinguish voxels and regions having different intensities of activation. Our suggested approach is evaluated in realistic two- and three-dimensional simulation experiments as well as on multiple real-world datasets. Finally, the value of our suggested approach in low-signal and single-subject fMRI studies is illustrated on a sports imagination experiment that is often used to detect awareness and improve treatment in patients in persistent vegetative state (PVS). Our ability to reliably distinguish activation in this experiment potentially opens the door to the adoption of fMRI as a clinical tool for the improved treatment and therapy of PVS survivors and other patients.