bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 16 citations · 37 references
MedicineNeurobiologyNeuroscienceDiagnostic ImagingNeural CircuitryVisual NeuroscienceBrain ImagingFunctional NeuroimagingMagnetic Resonance ImagingMedical ImagingPrimary Visual CortexBrain Function MappingBrain Functional ImagingCortical MappingElectrophysiological RecordingsMulti-echo SequencesNeuroscience ImagingPrimary Visual Cortex FunctionVoxels
Abstract Our understanding of the human brain relies on advancing noninvasive brain imaging approaches. Characterization of the function of brain circuitry depends on the spatiotemporal correspondence at which recorded signals can be mapped onto underlying neuronal structures and processes. Here we aimed to address key first-stage questions of feasibility, reliability, and utility of line-scanning fMRI as a next generation non-invasive imaging method for human neuroscience research at the mesoscopic scale. Line-scanning can achieve high spatial resolution by employing anisotropic voxels aligned to cortical layers. The method can simultaneously achieve high temporal resolution by limiting acquisition to a very small patch of cortex which is repeatedly acquired as a single frequency-encoded k-space line. We developed multi-echo line-scanning procedures to record cortical layers in humans at high spatial (200 μm) and temporal resolution (100 ms) using ultra high-field 7T fMRI. Quantitative mapping allowed us to identify cortical layers in primary visual cortex (V1) and record functional signals from them while participants viewed movie clips. Analysis of these recordings revealed layer-specific V1 spatial and orientation tuning properties analogous to those previously observed in electrophysiological recordings of non-human primates. We have consequently demonstrated that line-scanning is a powerful non-invasive imaging technique for investigating mesoscopic functional circuits in human cortex. Graphical Abstract
37
Cortical Surface-Based Analysis
Anders M. Dale, Bruce Fischl, Martin I. Sereno · NeuroImage · 1999 · 11.5K citations