Resting State Functional Connectivity is Sensitive to Layer-specific Connectional Architecture in Cortical Columns
Yun Wang1, Jennifer Robinson1,2,3, and Gopikrishna Deshpande1,2,3

1AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, 2Department of Psychology, Auburn University, Auburn, AL, United States, 3Alabama Advanced Imaging Consortium,Auburn University and University of Alabama Birmingham, Birmingham, AL, United States

Synopsis

We investigated whether resting-state functional connectivity (FC) is sensitive to cortical layer-specific connectional differences using high resolution resting-state fMRI data obtained from healthy humans at 7T. Based on rat tracing studies, we hypothesized that FC between the thalamus and cortical layer I must be significantly greater than between the thalamus and other layers. Our results support this hypothesis. Further, there were no global connectivity differences between layers, ruling out artifactual influences from vasculature. This also opens the future possibility of microscopic investigations of the brain connectome using ultra-high field fMRI and will likely move the field away from blobology.

Introduction

A cortical column is a complex processing unit that links a number of inputs to a number of outputs via overlapping internal processing chains [1]. Different layers in the column have different distribution and types of neurons as well as distinct connections with other cortical and subcortical regions. Our knowledge about the cortical laminar-specific functions has mostly come from invasive studies given that non-invasive modalities such as functional magnetic resonance imaging (fMRI) lacked the resolution to resolve layer-specific differences. However, recent advances in ultra-high field, high resolution fMRI has informed us about layer-specific fMRI responses to external stimuli [2]. But, it is yet unclear whether resting state functional connectivity (FC), a popular approach for investigating the brain’s connectome, is sensitive to layer-specific connectomic differences. We investigated this aspect with high resolution rs-fMRI data obtained at 7T. Specifically, we tested the hypothesis that FC between the thalamus and cortical layer I must be significantly greater than between the thalamus and other layers. This follows from evidence in rat brain tracing studies, which show that regions across the cortex receive inputs to layer I from M-type thalamus cells distributed in each nucleus of thalamus [3].

Methods

High resolution resting state fMRI data was obtained from twenty healthy individuals using an EPI sequence with the following parameters: 37 slices acquired parallel to the AC-PC line, 0.85 mm× 0.85 mm× 1.5 mm voxels, TR/TE: 3,000/28ms, 70º flip angle, base/phase resolution 234/100, A→P phase encode direction, iPAT GRAPPA acceleration factor=3, interleaved acquisition, 100 time points. Data were acquired on a Siemens 7T MAGNETOM outfitted with a 32-channel head coil by Nova Medical (Wilmington, MA). A whole-brain high-resolution three-dimensional (3D) MPRAGE sequence (256 slices, 0.63 mm × 0.63 mm × 0.60 mm, TR/TE: 2,200/2.8, 7º flip angle, base/phase resolution 384/100%, collected in an ascending fashion, acquisition time=14:06) was used to acquire anatomical data. FMRI preprocessing procedures included motion correction, slice timing correction, detrending and removal of nuisance variance in the data using time series from white matter, CSF and 6 motion parameters. Cortical surface reconstructions of the interface of white/gray matter and interface of gray matter/pial surface were automatically generated from 0.63mm isotropic anatomical data using FreeSurfer V6 beta. Six laminar surfaces (Fig.1) were estimated with relative distance to the two interfaces [5]. We employed a boundary-based registration method to register the interface of EPI white/gray matter to the corresponding surface reconstruction from the anatomical data [6]. Each voxel in the functional volume was then transferred onto the collection of 6 laminar surfaces using the transformation above, with correction of partial volume effects. We obtained the automatically generated cortical parcellation using Desikan-Killiany (DK) Atlas [7] and subcortical segmentation for each subject. An average time series was extracted from the thalamus as well as 34 cortical ROIs in the DK atlas separately for left and right brain in each subject. The 68 mean time series corresponding to the cortical ROIs were extracted for each of the 6 layers. The corresponding analysis pipeline is shown in Fig.2. FC was calculated between all 68 ROIs both within and across layers in order to investigate global layer-specific trends. Finally FC was calculated between the thalamus and the 68 ROIs in each of the 6 layers for investigating the hypothesis stated above.

Results and Discussion

The mean correlation between a given layer and all layers did not show any significant difference between layers (Fig.3). This demonstrates that global connectivity differences between layers, potentially influenced by vasculature and/or physiological noise, were absent. The functional connectivity pattern for thalamo-cortical connections showed that the correlation between layer I and the thalamus was strongest across the cortex (Fig.4), significantly (FDR corrected p<0.05) more than the correlation between the thalamus and layers II-VI. Although layer IV showed a trend to be more strongly connected to the thalamus, it did not reach significance. These findings demonstrate support for the hypothesis that resting state FC is sensitive to layer specific connectional architecture in cortical columns in general, and specifically sensitive to thalamo-cortical projections into layer I. This also opens the future possibility of microscopic investigations of the brain connectome using ultra-high field fMRI and will likely move the field away from blobology [8-9].

Acknowledgements

No acknowledgement found.

References

[1] Mountcastle, Brain; 120:701–22, 1997. [2] Olman, et al, PLoS One;7( 3): e32536, 2012. [3] Rubio-Garrido, Cereb. Cortex;19(10): 2380–2395, 2009. [4] E. G. Jones, Neuroscience;85(2): 331–345, 1998. [5] Polimeni, Neuroimage; 52(4):1334–1346, 2010. [6] Greve and Fischl, Neuroimage;48(1): 63–72, 2009. [7] Desikan, Neuroimage;31(3): 968–80, 2006. [8] Poldrack, NeuroImage; 62(2), 1216–1220, 2014. [9] Turner, Brain connectivity; 4(7): 547-557, 2014.

Figures

Figure 1. Illustration of the 6 laminar surfaces (yellow: gray matter – white matter boundary, red: pial surface)

Figure 2. A schematic of the analysis pipeline for extracting mean time series from the six cortical layers for all 68 brain regions in the DK atlas

Figure 3. Functional connectivity within and between the six cortical layers

Figure 4. Functional connectivity between the thalamus and all six cortical layers



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
0636