Cortical depth-dependent human fMRI of resting-state networks using EPIK

Abstract

Recent laminar-fMRI studies have provided substantial understanding of the evoked cortical responses in multiple sub-systems; in contrast, the laminar component of resting-state networks remains largely unknown. Here, we used EPIK, which offers unprecedented coverage at sub-millimetre resolution, to investigate cortical resting-state dynamics with depth specificity. After verifying laminar activation profiles in response to a task, we investigated whole-brain laminar dynamics. During rest, signal oscillations were stronger in the superficial layers of the cortical ribbon, and functional connectivity analysis indicated that most resting-state cortical connections occurring at rest involve supra-granular layers. Coherence assessed in a lateral motor network suggested a modified distribution of layer-specific responses during task performance compared to resting-state. Whole-brain evaluation of laminar-fMRI encompasses unprecedented computational challenges; nonetheless, it enables a new dimension of the human cerebral cortex to be investigated from a global view, which may be key in the characterization of neurological disorders from a novel perspective.Significance statementResting-state networks sustain conscious awareness and diverse cognitive processing in the absence of tasks. In contrast to cortical areas, the cortical depth-specific signals across different networks have been poorly investigated, mainly due to the small brain coverage enforced in high-resolution imaging methods. Here, we demonstrate the different involvement of the cortical layers in the maintenance of the resting-state dynamics at near whole-brain level, using an optimized fMRI sequence. Given the cytoarchitectonics of the human neocortex, and based on our results, the cortical thickness constitutes an important dimension to characterize the resting-state oscillations in the healthy brain and its functional study may facilitate the identification of novel targets in neurological diseases.

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

“…There are indications of feedback signatures in: V1-V2, V1-V3, V1-V4, and V1-MT. Also overall, we find the largest similarities between superficial layers compared to deeper layers (Guidi et al 2020; Huber et al 2017; Deshpande, Wang, and Robinson 2022; Pais-Roldán et al 2020). This might indicate that feedback signals dominate the temporal signal fluctuations during repeated movie watching.…”

“…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.…”

“…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.…”

Feasibility of 3T layer-dependent fMRI with GE-BOLD using NORDIC and phase regression

“…While NORDICmicro profiles from 2 of 5 subjects in the present experiment consistently had the double peak feature (Figure 2), the group profile can better be described by a superficial peak with a shoulder in the deep layers (Figure 5B). Although we do believe that the lack of two distinct peaks is partly explained by macrovascular influence, it may also be explained by the fact that we extract signal from relatively large ROIs spanning several slices (Han et al, 2021; Pais-Roldán et al, 2020). Moreover, not all subjects have double peak profiles even with non-GE-BOLD sequences (Beckett et al, 2020; Shao et al, 2021), thus we cannot rule out that more distinct peaks would emerge in the group profile with different subjects.…”