Decoding of columnar-level organization across cortical depth using BOLD- and CBV-fMRI at 7 T.

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Human Brain Imaging

Background:

• Functional magnetic resonance imaging (fMRI) measures hemodynamic responses, limited by macrovascular signals that reduce spatial specificity.
• Multivariate pattern analysis (MVPA) leverages multi-voxel information to retrieve fine-grained spatial patterns from neurophysiological data.
• Imaging cortical structures at the scale of columns and layers is challenging due to fMRI signal limitations.

Purpose of the Study:

• To examine the spatial specificity of signals used by MVPA in high-resolution fMRI.
• To assess the impact of macrovascular contributions on MVPA decoding across cortical layers.
• To compare different fMRI acquisition techniques for their ability to resolve laminar information.

Main Methods:

• Acquisition of 7 Tesla (7T) fMRI data measuring ocular dominance columns (ODCs) in the human primary visual cortex (V1).
• Utilized gradient echo-based BOLD (GE-BOLD), spin echo-based BOLD (SE-BOLD), and vascular-space-occupancy (VASO) fMRI techniques.
• Decoding eye-of-origin information from signals across cortical layers using MVPA.

Main Results:

• Ocularity information was successfully decoded using all tested fMRI acquisition techniques.
• Laminar profiles indicated that macrovascular contributions universally affected all methods, limiting specificity across cortical depth.
• MVPA's ability to resolve fine-grained patterns is constrained by inherent signal limitations.

Conclusions:

• While MVPA is valuable for studying mesoscopic human cortical circuitry, its spatial specificity is compromised by macrovascular signal contributions.
• Careful consideration of macrovascular effects is crucial for accurate interpretation of MVPA results in laminar fMRI studies.
• Further development of fMRI techniques is needed to improve spatial resolution at the cortical layer level.