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Introduction

Network neuroscience analyzes the connections between regions of the brain to assess the community structure of those connections and the role of different regions in communication across those communities. The regions in these communities may be defined by one of many existing parcellation definitions, which can be highly variable in their resolution and topography. Previous research has shown that choice of parcellation can influence network measures (Messé, 2019). Here, we asked if parcellation resolution influenced the community affiliated with a given brain location, and whether this related to changes in the average hubness of the regions in each community.

Methods

To assess how parcellation resolution influenced community assignment, we defined a set of surface-based coordinates of interest (COIs) and analyzed how many unique communities each was affiliated with across resolutions. To identify our COIs, we projected spherical volumes centered on a set of point locations (Power et al, 2011) onto a surface space and manually selected the coordinate located most centrally within the projected area. For the assignment analysis, we utilized the Global-Local parcellation set (Schaefer et al, 2018) in ten different resolutions from 100 to 1000 regions, each of which has each been mapped to a set of seven communities. We identified the parcel containing each of our COIs and its affiliated community at each resolution, then calculated each COI’s number of unique communities across resolutions. To assess how resolution influenced hubness measures of these communities, we applied a 17-community structure (Yeo et al, 2011) to the resting state data of 50 subjects from the Human Connectome Project and calculated coordinate-level metrics of participation coefficient (PC) and within-module degree (WMD). We assessed the PC and WMD for each parcel at each resolution by averaging across the values of all of the coordinates contained in that parcel. Finally, we assessed the trends in PC and WMD within each community across resolutions using repeated measures ANOVAs.

Results

Our network assignment analysis revealed that 161 (66.8%) out of the 241 COIs were affiliated with a single community across resolutions. Of the remaining 80 regions, 64 (79.6%) were affiliated with two communities; 15 (18.8%) with three; and one with four. Mapping out the affiliations between communities revealed a dense sharing structure. The somatomotor network (SMN) was the most isolated, sharing regions almost exclusively with the dorsal (DAN) and salience/ventral attention (SVAN) networks. The visual network primarily shared regions with the DAN, while the limbic system primarily shared with the default mode network (DMN). Higher-order association networks showed a rich pattern of mutual sharing. PC and WMD showed largely consistent values across resolutions in the control, limbic, DAN, SVAN, SMN, and visual networks. The DMN showed linear relationships for both WMD (F = 7.425, p = .009, η_p² = .134) and PC (F = 6.035, p = .018, η_p² = .112). That is, DMN appeared more internally integrated, but more externally isolated, with increasing parcel resolution.

Conclusions

The current results show that community affiliation is critically dependent on parcellation resolution while community-level network measures are not, which suggests that each parcellation is capturing different facets of a location’s connectivity based on which connections are included in the parcel definition. They also indicate that regions shift community affiliation along a constrained set of paths that are not described by aggregate measures of hubness. Together, this suggests that studies using a single parcellation to assign regions to unique communities do not accurately or fully capture the brain’s community structure, severely limiting their capacity to interpret regional roles within the network.

References

• Messé, A. (2019). Parcellation influence on the connectivity-based structure–function relationship in the human brain. Human Brain Mapping, (November), 1–14.
• Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., … Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.
• Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.
• Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Petersen, S. E. (2011). Functional Network Organization of the Human Brain. Neuron, 72(4), 665–678.

Supplementary Materials *Updated 19 June 2020

Contents:

1. Reproduction of switching map with networks color-coded

2. Visualization of switching ROIs

3. Number of ROIs switching between specific network pairs

4. Graphs of network connectivity measures by number of networks per region

1. Reproduction of the map of the number of regions that change assignment between networks across resolution with networks color coded. Color coding follows the original scheme described by Yeo and colleagues (2011), also reproduced here. 100-parcel topographic map provided for reference.

2. Video showing ROIs that switch network membership across parcellation resolutions, sagittal view. Video starts at the leftmost slice.

3. Number of ROIs that switch between specific network pairs:

4. Graphs of average participation coefficient and within-module degree for regions assigned to one, two, three, and four networks across parcellation resolutions. Participation coefficient indexes the distribution of connections a given region shares with networks outside of its own; within-module degree indexes the strength of its connection within its own network. Statistics were run for network counts between 1 and 3, as only one region was assigned to 4 networks.

Participation Coefficient

Participation coefficient significantly increased from regions in one network to those in multiple networks (p_1,2 & p_1,3 < .001). This suggests that regions that were assigned to different networks across resolutions have stronger connections to more than one network than those that were consistently assigned to one network.

Within-Module Degree

Within-module degree significantly decreased from regions in one network to those in multiple networks (p_1,2 = .005; p_1,3 = .012). This suggests that regions that were assigned to different networks across resolutions are more weakly connected to their assigned network than those that were consistently assigned to one network.

Mapping Systematic Changes in Community Assignment Across Parcellation Resolutions

Full list of poster Jitsis

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Abstract

Poster Reprint

Supplementary Results

Network neuroscience often relies on the use of pre-existing parcellations to define the boundaries of the brain regions that will be submitted for network assignment and subsequent analysis. We present preliminary results that indicate that the topography of network partitions varies with the number of regions included in the parcellation when the parcellation algorithm is kept constant. This may be due to implicit assumptions in network assignment methods that lose important information about the multi-network connections of many regions, especially those showing “hub-like” properties. We argue that network neuroscience will need to move toward new methods that capture this information in order to gain a full understanding of the relationship between regional processes and network communication structure.

OHBM attendees can see this work presented live on Zoom at the following dates and times (24hr format). Zoom link will be provided in the live chat window during each session.

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OHBM 2020

Parcellations

I’m a visiting scholar in Dr. Mark D’Esposito’s lab at the University of California, Berkeley, having completed my postdoctoral fellowship in December 2022. I am trained as a cognitive neuroscientist and experimental psychologist. My research aims to understand how the different scales of functional organization observed in the brain – for example, regional and network-level – work together to support complex behavior.

I completed my undergraduate degree in Biomedical Engineering at the Georgia Institute of Technology in 2011. As an undergrad, I worked as a research assistant with Dr. Michelle LaPlaca developing in vitro models of secondary pathways of cell damage response. This led me to an interest in cognitive function and its underlying neural mechanisms.

In Fall 2011, I started graduate school with Dr. Eric Schumacher in the School of Psychology at the Georgia Institute of Technology. My research focused on how brain regions controlled preparation and execution of tasks, especially in the lateral frontal cortex. This research combined experimental psychology and functional magnetic resonance imaging methods in order to link behavior with brain activity in real time.

To further explore the interactions between functional regions, in my postdoctoral research I turned toward graph theory and network neuroscience, using novel methods that allow regions to be a part of multiple overlapping networks. I received NIH F32 funding in January of 2020 to pursue a set of projects exploring these overlapping networks. In the future, I hope to understand how brain networks relate to activation of individual regions in the lateral frontal cortex, and how temporary disruption of these different regions via transcranial magnetic stimulation can lead to network-level connectivity changes.