Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols

Abstract

Resting‐state functional magnetic resonance imaging (fMRI) has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 × 0.7 × 0.7 mm 3 , with only partial brain coverage. Therefore, this work aims to present a novel fMRI technique that was developed based on echo‐planar‐imaging with keyhole (EPIK) combined with repetition‐time‐external (TR‐external) EPI phase correction. Each technique has been previously shown to be effective in enhancing the spatial resolution of fMRI, and in this work, the combination of the two techniques into TR‐external EPIK provided a nominal spatial resolution of 0.51 × 0.51 × 1.00 mm 3 (0.26 mm 3 voxel) with whole‐cerebrum coverage. Here, the feasibility of using half‐millimetre in‐plane TR‐external EPIK for resting‐state fMRI was validated using 13 healthy subjects and the corresponding reproducible mapping of resting‐state networks was demonstrated. Furthermore, TR‐external EPIK enabled the identification of various resting‐state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high‐resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.

Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset

“…Early attempts of layer-fMRI connectivity studies have been somewhat limited to relatively small field of views, constrained to individual brain systems (Polimeni et al 2010; Guidi et al 2020; Huber et al 2017; Huber, Finn, et al 2020). More recent advancements in data sampling approaches, MR-contrast generation strategies, and confidence of the laminar signal interpretability allowed proof-of-principle extensions of layer-fMRI connectivity to larger FOV (Sharoh et al 2019; Huber et al 2021; Deshpande, Wang, et al 2022; Pais-Roldán et al 2020; Yun et al 2022). In this work, we aim to help the layer-fMRI community in building tools to make such whole-brain layer-fMRI connectivity protocols usable for neuroscience application studies.…”

“…This means that focusing on one specific frequency window is expected to provide results that are largely representative of functional connections at any other temporal scale. Ongoing work in combining the whole-brain layer-fMRI approaches with further advanced hardware (Feinberg et al 2022; Beckett et al 2022) and MR-reconstruction (Yun et al 2022) will become important to confirm this temporal invariance.…”

“…Previous large coverage layer-fMRI acquisition protocols exist and exemplify that current acquisition tools are -in principle-sensitive to capture depth-dependent connectivity differences (Finn et al 2021; Bandettini et al 2021; Huber et al 2021; Yun et al 2022; Pais-Roldán et al 2020; Sharoh et al 2019; Chang et al 2022; Deshpande, Zhao, et al 2022; Müller et al 2021). Here, we move beyond the proof-of-principle concept and characterize the stability of such acquisition protocols, we developed a preprocessing protocol, and we explored the neuroscientific usability of whole-brain layer-fMRI.…”

“…Previous high-resolution studies have convincingly shown that layer-fMRI acquisition protocols can be increased to capture large patches of the cortex (Finn, Huber, and Bandettini 2021; Bandettini, Huber, and Finn 2021; Huber et al 2021; Yun et al 2022; Pais-Roldán et al 2020; Sharoh et al 2019; Chang et al 2022; Deshpande, Zhao, and Robinson 2022; Müller et al 2021). Each of these articles shows that there are functional connectivity signal modulations across cortical depth that layer-fMRI can capture.…”

Contribution of cortical layers to human dynamic functional connectivity

“…To date, largely owing to a lack of methods to image the whole brain with high spatial resolution and sufficient coverage, the literature has focused on region-of-interest (ROI)-based network analysis (static or dynamic), disregarding the depth at which the connections take place in the cortex. Recently, novel highresolution fMRI sequences have allowed us not only to detect the connections between remote functionallydifferent areas but to do so with sub-millimetre resolution, enabling the evaluation of resting-state networks at multiple cortical depths (Huber et al, 2020, Yun and Shah, 2017, Yun et al, 2020, Yun and al., 2021, Pais-Roldán et al, 2020, Pais-Roldán et al, 2023, Yun et al, 2022. For instance, we have previously shown that, in healthy subjects, areas of the default mode network (DMN) are strongly correlated in the superficial layers, while the central executive control network (CEN) involves connections between deeper cortical territories (Pais-Roldán et al, 2023).…”