Cerebral blood volume (CBV) and cerebral blood flow (CBF)-based functional magnetic resonance imaging (fMRI) have proven to be more laminar-specific than blood-oxygen-level-dependent (BOLD) contrast fMRI, but they suffer from relatively low sensitivity. In previous work, we integrated CBV and CBF into one contrast using DANTE (Delay Alternating with Nutation for Tailored Excitation) pulse trains combined with 3D echo-planar imaging (EPI) to create an integrated blood volume and perfusion (VAPER)-weighted contrast (Chai et al., 2020). Building on this, we have now introduced a magnetization transfer approach to induce a tissue-suppression-based VASO (vascular space occupancy) effect and incorporated it with the VAPER technique to boost the overall sensitivity while maintaining superior laminar specificity, all without altering the original VAPER sequence timing scheme. This magnetization transfer (MT)–VAPER fMRI acquisition alternates between DANTE blood-nulling and MT-tissue-suppression conditions, generating an integrated VASO and perfusion contrast enhanced by MT. Both theoretical and experimental evaluation demonstrated an approximately 30% enhancement in VAPER sensitivity with MT application. This novel MT–VAPER method was empirically validated in human primary motor and visual cortices, demonstrating its superior laminar specificity and robust reproducibility, establishing it as valuable non-BOLD tool for laminar fMRI in human brain function research.

Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis of neuroanatomical microstructure, invaluable for clinical and population studies. However, many measurements with different diffusion-encoding directions and possibly b -values are necessary to infer the underlying tissue microstructure within different imaging voxels accurately. Two challenges particularly limit the utility of dMRI: long acquisition times limit feasible scans to only a few directional measurements, and the heterogeneity of acquisition schemes across studies makes it difficult to combine datasets. Left unaddressed by previous learning-based methods that only accept dMRI data adhering to the specific acquisition scheme used for training, there is a need for methods that accept and predict signals for arbitrary diffusion encodings. Addressing these challenges, we describe the first geometric deep learning method for continuous dMRI signal reconstruction for arbitrary diffusion sampling schemes for both the input and output. Our method combines the reconstruction accuracy and robustness of previous learning-based methods with the flexibility of model-based methods, for example, spherical harmonics or SHORE. We demonstrate that our method outperforms model-based methods and performs on par with discrete learning-based methods on single-, multi-shell, and grid-based diffusion MRI datasets. Relevant for dMRI-derived analyses, we show that our reconstruction translates to higher-quality estimates of frequently used microstructure models compared to other reconstruction methods, enabling high-quality analyses even from very short dMRI acquisitions.

Advances in ultra-high field 7 Tesla functional magnetic resonance imaging (7 T fMRI) have provided unprecedented opportunities to gain insights into the neural underpinnings supporting human memory. The hippocampus, a heterogeneous brain structure comprising several subfields, plays a central role during vivid re-experiencing of autobiographical memories (AM). However, due to technical limitations, how hippocampal subfields differentially support AM, whether this contribution is specific to one portion along the hippocampal long-axis, and how subfields are functionally connected with other brain regions typically associated with AM retrieval remains elusive. Here, we leveraged technical advances of parallel imaging and employed a submillimeter Echo Planar Imaging sequence over the whole brain while participants re-experienced vivid, detail-rich AM. We found that all hippocampal subfields along the long-axis were engaged during AM retrieval. Nonetheless, only the pre/parasubiculum within the anterior body of the hippocampus contributed over and above to AM retrieval. Moreover, whole-brain functional connectivity analyses of the same data revealed that this part of the hippocampus was the only one that was strongly connected to other brain regions typically associated with AM, such as the ventromedial prefrontal cortex (vmPFC) and medial/lateral parietal regions. In the context of the broader literature, our results support recent proposals that the anterior body of the pre/parasubiculum may play an important role in scene-based cognition, such as its engagement during the re-experiencing of personal past events.