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Layer ReHo: Tool for characterizing mesoscale functional structure across layers and columns
Burak Akin1, Richard Klein1, Kenshu Koiso2, Yinghua Yu3, JiaJia Yang3, Renzo Huber1, and Peter Bandettini1
1NIMH, NIH, Bethesda, MD, United States, 2Faculty of Psychology and Neuroscience, Maastricht, Netherlands, 3Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan

Synopsis

Keywords: Functional Connectivity, High-Field MRI, layer fMRI, ReHo

Motivation: High resolution fMRI is an emerging field, analysis pipelines are still under development. Local and short distanced time course similarities also named as Layer dependent regional homogeneity, can give us insights to map laminar dependent signal differences.

Goal(s): Using a simple metric to color code the depth dependent cortical activity.

Approach: Using mesoscale functional structure to predict and understand, time locked activity patterns in cortical ribbon without having necessity of specific model or predefined ROI

Results: Layer dependent regional homogeneity predicts local activity patterns, also can be used as a similarity matrix to rank different runs which potentially contributes to similar layer patterns.

Impact: This work can potentially be a publicly available analysis tool to observe and quality check, can also quantify laminar separability of the acquired depth dependent high resolution fMRI data.

Introduction

With the advance of high field scanners and the novel imaging methods, sub-millimeter fMRI acquisitions have become more common(Huber et al. 2018). Although a whole brain layer dataset(Koiso et al. 2023) recently became available, most of the high resolution fMRI studies are restricted to confined cortical regions like somatomotor(Yang et al. 2021; Yu et al. 2022) and visual areas(Akbari et al. 2021; Cho et al. 2022; Mourik et al. 2021). There is an increasing interest in confined activation patterns like layer dependent signals(Huber, Finn, et al. 2021; Müller et al. 2021). However investigating local and short distance connectivity structures at higher resolutions hasn't been studied and it could be a model free way to map and delineate confined functional structures. In this study we developed a simple data processing method called layer dependent regional homogeneity (Layer ReHo) to investigate above mentioned possibilites.

Methods

ReHo is defined and used previously in conventional fMRI resolutions(Jiang and Zuo 2016),also in one high resolution study(Pais-Roldán et al. 2020). Similarly we rank the time course similarity of the neighboring voxels. Differently, we restrict the neighbors to a certain cortical depth, a.k.a. layer bins calculated by open software called LayNii (Huber, Poser, et al. 2021). We calculated the similarity of each layer bins and values are given to corresponding voxels, see Fig1.

Results & Conclusion

We’ve compared ReHo values calculated in somatomotor cortex with the activation pattern of fingers(index, middle, ring and pinky) stimulated vibrotactile setup described in the study(Yu et al. 2019) The percent activity change in finger areas show very high spatial resemblance with ReHo maps. See Fig2 & 3. We also calculate ReHo maps with publicly available whole brain dataset (https://openneuro.org/datasets/ds003216/versions/3.0.11) which consist of 50 individual runs of movie watching acquired in 10 different sessions, initial results have shown that, ReHo maps are highly similar across the runs. See Fig4. similar ReHo patterns across runs and days indicating that ReHo maps are picking up movie watching activity which are expected to be somewhat similar across runs.

Acknowledgements

No acknowledgement found.

References

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  • Müller, Anna Katharina, Miriam Heynckes, J. Wiggins, Gulban Christopher, Omer Faruk, Yuhui Chai, Benedikt A. Poser, and Laurentius Huber. 2021. “Whole Brain Layer-FMRI: An Open Dataset for Methods Benchmarking.”
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Figures

Figure 1; An example of Layer dependent ReHo(Regional Homogeneity) calculation ; Time courses are extracted from the same laminar bin and correlation coefficients across all pairs are calculated. A square matrix with a size of the number of voxels is averaged for each column. This gives a coefficient or in other words weight of that particular voxel to the laminar bin which is given to the voxel as a ReHo value.

Figure 2; Representative Layer ReHo a), and activation patterns b) from three subjects.

FIg3 Scatter plots of local percent activity vs ReHo local correlation values

Fig4. ReHo similarity across 50 runs of whole brain dataset.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
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DOI: https://doi.org/10.58530/2024/3124