Indirect structural-functional connectivity coupling in healthy adult brain networks
Poster No:
1705
Submission Type:
Abstract Submission
Authors:
Kevin Solar1, Kierra Murphy2, Sean Carter2, Teresa Figley2, Jennifer Kornelsen2, Chase Figley2
Institutions:
1Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Canada, 2Department of Radiology, University of Manitoba, Winnipeg, Canada
First Author:
Kevin Solar, BSc, MSc, PhD
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Co-Author(s):
Introduction:
The sensory, motor, and cognitive processes performed in the human brain rely on complex wiring (structural connectivity; SC) and communication (functional connectivity; FC) within macroscale networks (Suárez et al., 2020). Although prior studies suggest that SC strength predicts the existence and degree of FC between anatomically-defined brain regions, few have evaluated SC-FC relationships in large-scale networks, and even fewer have used functionally-defined network atlases to extract both SC and FC (Straathof et al., 2019). The purpose of this study was to compare SC (diffusion MRI) and FC (resting-state fMRI) strengths using functionally-defined atlases of the dorsal/ventral default mode (dDMN, vDMN), left/right executive control (lECN, rECN), and anterior/posterior salience networks (aSN, pSN).
Methods:
MRI data were obtained from 100 healthy adults (65 female, 35 male; age = 39±16 [range: 18-85] years) recruited through the Comorbidities and Cognition in Multiple Sclerosis Study control cohort (Uddin et al., 2022), sufficient for 80% power to detect correlations of ±0.285 or larger. Exclusion criteria included traumatic brain injury as well as neurological, psychiatric, or other chronic medical conditions.
All MRI data were collected using a 3T Siemens Trio and 32-channel head coil. High angular resolution diffusion imaging data (50 directions at b = 1500 s/mm2, 5 interleaved b = 0 s/mm2 images) were used for tensor parameter estimation to quantify SC (calculated as the additive inverse of mean diffusivity; -MD). Resting-state fMRI data (7 min eyes open) were used to quantify FC (calculated as bivariate temporal correlations between BOLD signals). For each network, SC was measured within functionally-defined white matter regions-of-interest (ROIs) using the UManitoba-JHU Functionally-Defined Human White Matter Atlases (Figley et al., 2015, 2017), while FC was measured between complementary functionally-defined nodes from the corresponding Stanford Resting State "90 functional-ROIs" fMRI Atlas (Shirer et al., 2012). Pearson correlations then assessed linear relationships between SC and FC, and post-hoc analyses examined the proportion of direct/indirect and positive/negative SC-FC coupling, as well as rich-club region involvement (Fig 1).
All MRI data were collected using a 3T Siemens Trio and 32-channel head coil. High angular resolution diffusion imaging data (50 directions at b = 1500 s/mm2, 5 interleaved b = 0 s/mm2 images) were used for tensor parameter estimation to quantify SC (calculated as the additive inverse of mean diffusivity; -MD). Resting-state fMRI data (7 min eyes open) were used to quantify FC (calculated as bivariate temporal correlations between BOLD signals). For each network, SC was measured within functionally-defined white matter regions-of-interest (ROIs) using the UManitoba-JHU Functionally-Defined Human White Matter Atlases (Figley et al., 2015, 2017), while FC was measured between complementary functionally-defined nodes from the corresponding Stanford Resting State "90 functional-ROIs" fMRI Atlas (Shirer et al., 2012). Pearson correlations then assessed linear relationships between SC and FC, and post-hoc analyses examined the proportion of direct/indirect and positive/negative SC-FC coupling, as well as rich-club region involvement (Fig 1).
Results:
Eight significant SC-FC correlations (p<0.05) were identified in the dDMN, including seven negative (one direct, six indirect) and one positive (indirect), and the vDMN showed no significant correlations. The lECN showed five significant negative (one direct, four indirect) correlations, and the rECN showed two positive (indirect) correlations. The aSN showed two significant negative (indirect) correlations, and the pSN showed three negative (indirect) and one positive (indirect) correlations (Fig 2). Overall, 91% of significant SC-FC relationships were indirect, and 81% were negative. Rich-club regions (e.g., thalamus, parahippocampal gyrus) were involved in 67% of significant SC-FC connections.
Conclusions:
The importance of these results is that they advance the characterization of healthy adult SC-FC coupling profiles within key neural networks using complementary structural and functional atlases. Largely indirect SC-FC coupling within the dDMN, lECN, rECN, aSN, and pSN supports the concept that FC commonly arises from multi-synaptic communication and is not constrained by, and indeed commonly diverges from underlying direct SC (Avena-Koenigsberger et al., 2018; Mišić et al., 2016). Moreover, the disproportionate involvement of rich-club regions aligns with higher rates of indirect SC-FC coupling involving transmodal (higher order) brain regions relative to unimodal (sensory) areas (Baum et al., 2020). By defining normal SC-FC coupling strictly within functionally-defined connections, rather than more common whole-brain exploratory analyses (Suárez et al., 2020), our method may provide a higher sensitivity to functional impairments related to abnormal SC-FC coupling.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 1
Keywords:
FUNCTIONAL MRI
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Connectivity; Network; Structure–function
1|2Indicates the priority used for review
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