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Updated: Feb 26, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
Non-BOLD contrast for laminar fMRI in humans: CBF, CBV, and CMRO2
Laurentius Huber1, Kâmil Uludağ2, Harald E Möller3
1sFIM, NIMH, NIH, Bethesda, MD, USA.
Abstract:
Functional magnetic resonance imaging (fMRI) using the blood oxygenation level-dependent (BOLD) contrast indirectly probes neuronal activity changes via evoked cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen (CMRO2) changes. The gradient-echo BOLD signal is mostly sensitive to ascending veins in the tissue and to pial veins. Thereby, the achievable spatial specificity to neuronal activation is limited. Furthermore, the non-linear interaction of CBF, CBV and CMRO2 can hamper quantitative interpretations of the BOLD signal across cortical depths with different baseline physiology. Measuring CBF, CBV or CMRO2 directly on a depth-dependent level has the potential to overcome these limitations. Here, we review these candidates of physiologically well-defined contrasts with the particular focus on arterial spin labeling (ASL), vascular space occupancy (VASO) and calibrated fMRI. These methods are reviewed with respect to their fMRI sequence parameter space and the applicability for neuroscientific studies in humans. We show representative results of depth-dependent 'non-BOLD-fMRI' in humans and their spatiotemporal characteristics. We conclude that non-BOLD methods are promising alternatives compared to conventional fMRI as they can provide improved spatial specificity, quantifiability and, hence, physiological interpretability as a function of cortical depth. At submillimeter resolution with inherently low signal-to-noise ratio (SNR), however, their use is still challenging. Nevertheless, we believe that 'non-BOLD-fMRI' is a useful alternative for depth-dependent investigations, by providing valuable insights into neurovascular coupling models that facilitate the interpretability of fMRI for neuroscientific applications.
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