Precision fMRI and cluster‐failure in the individual brain

• Published in: Human brain mapping Vol. 45; no. 12; pp. e26813 - n/a
• Main Authors: Ceja, Igor Fabian Tellez, Gladytz, Thomas, Starke, Ludger, Tabelow, Karsten, Niendorf, Thoralf, Reimann, Henning Matthias
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.08.2024
• Subjects: adaptive weights smoothing; Adult; Blood levels; BOLD; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain research; Cluster Analysis; cluster failure; Cost analysis; Error correction; fMRI; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Inference; Magnetic fields; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Neuroimaging; Neurosciences; Oxygen - blood; Sensitivity analysis; Signal integrity; Signal to noise ratio; single‐subject; Smoothing; spatial accuracy; Spatial discrimination; Spatial resolution; Spatial smoothing; Statistical inference; Temporal resolution; spatial smoothing; adaptive weights smoothing; fMRI; cluster failure; single‐subject; spatial accuracy; BOLD
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26813

Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level‐dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single‐subject analysis. We introduce adaptive‐weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster‐corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole‐brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family‐wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download. Functional magnetic resonance imaging (fMRI) at ≥7 T achieves finer spatial resolution, eventually causing a decrement in temporal signal‐to‐noise ratio and thus blood oxygen level‐dependent (BOLD) sensitivity. Adaptive‐weight smoothing with optimized metrics (AWSOM) enhances BOLD effects detection with high spatial accuracy and preserves the integrity of BOLD signal magnitudes. AWSOM minimizes family‐wise error rates by effectively suppressing false positives in single‐subject fMRI analysis.