Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

Abstract

The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left-and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.

Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014)

“…While the individual-level implementation of the autonomy index was found to be reliable, task effects were found. This finding replicates previously observed task effects on a different measure of functional connectivity-derived network specialization (see Peterson et al, 2023). This is likely due to the idea that the act of “resting” introduces greater variability in functional connectivity compared to that observed during task-based fMRI, potentially as a result of mind wandering (Elton & Gao, 2015; Finn & Bandettini, 2021).…”

“…(2019) 17-network individual parcellation identified these two networks as the most specialized or lateralized (see Peterson et al, 2023). While it is unclear why the language and visuospatial attention networks were not identified as the most specialized networks, this finding may be due to the larger number of networks examined here, compared with the seven networks examined by Wang et al (2014).…”

“…Each dataset was selected for its relatively high quantity of low-motion data per participant. See Peterson et al (2023) for dataset descriptions and accompanying MRI acquisition parameters.…”

“…Preprocessing took place on raw NIFTI files for the resting-state fMRI and task fMRI runs using a pipeline developed by the Computational Brain Imaging Group (CBIG; Kong et al, 2019; Li et al, 2019). The implementation of this pipeline was described previously (see Peterson et al, 2023) and is summarized briefly here. Following FreeSurfer surface reconstruction (FreeSurfer 6.0.1, RRID:SCR_001847; Dale et al, 1999), the pipeline includes the removal of the first four frames and motion correction (using FSL, RRID:SCR_002823; (Jenkinson et al, 2002; Smith et al, 2004), functional and structural image alignment (using FreeSurfer’s FsFast; Greve & Fischl, 2009), linear regression using multiple nuisance regressors (using a combination of CBIG in-house scripts and FSL MCFLIRT; Jenkinson et al, 2002), bandpass filtering (using CBIG in-house scripts), surface projection (using FreeSurfer’s mri-vol2surf function), and surface smoothing using a 6 mm full-width half-maximum kernel (using FreeSurfer’s mri_surf2surf function; Fischl et al, 1999).…”

“…Task Effects on the Autonomy Index. Following the procedure outlined in Peterson et al (2023), task effects were also examined for the estimation of the autonomy index within individuals. Briefly, individual parcellations were generated using both task and resting-state fMRI data from the NSD dataset using various combinations of runs within task type: evennumbered runs, odd-numbered runs, the first half of runs, the second half of runs, and two random selections of runs (without replacement).…”