Macro-scale patterns in functional connectivity associated with ongoing thought patterns and dispositional traits

Abstract

Complex macro-scale patterns of brain activity that emerge during periods of wakeful rest provide insight into the organisation of neural function, how these differentiate individuals based on their traits, and the neural basis of different types of self-generated thoughts. Although brain activity during wakeful rest is valuable for understanding important features of human cognition, its unconstrained nature makes it difficult to disentangle neural features related to personality traits from those related to the thoughts occurring at rest. Our study builds on recent perspectives from work on ongoing conscious thought that highlight the interactions between three brain networks - ventral and dorsal attention networks, as well as the default mode network. We combined measures of personality with state-of-the-art indices of ongoing thoughts at rest and brain imaging analysis and explored whether this tripartite view can provide a framework within which to understand the contribution of states and traits to observed patterns of neural activity at rest. To capture macro-scale relationships between different brain systems, we calculated cortical gradients to describe brain organisation in a low dimensional space. Our analysis established that for more introverted individuals, regions of the ventral attention network were functionally more aligned to regions of the somatomotor system and the default mode network. At the same time, a pattern of detailed self-generated thought was associated with a decoupling of regions of dorsal attention from regions in the default mode network. Our study, therefore, establishes interactions between attention systems and the default mode network are important influences on ongoing thought at rest and highlights the value of integrating contemporary perspectives on conscious experience when understanding patterns of brain activity at rest.

Manifold Learning for fMRI time-varying FC

“…The Procrustes transformation has several applications in fMRI data analysis including hyper-alignment of voxel-wise responses (Haxby et al, 2011), alignment of macro-anatomical functional connectivity gradients (Margulies et al, 2016) and generation of bidirectional mappings between fMRI responses and natural language description of scenes in naturalistic experiments (Vodrahalli et al, 2018). The benefits of using dimensions beyond those being interpreted during alignment has been previously reported for functional gradients by McKeown and colleagues (2020) who showed that alignment towards a template space significantly improves when using 10 dimensions instead of three (the number of gradients often explored and interpreted in studies that rely on this technique (Hardikar et al, 2022;Margulies et al, 2016;Mckeown et al, 2020;Tian et al, 2020)). The supplementary materials from that study (Suppl.…”

Manifold learning for fMRI time-varying functional connectivity

“…The Procrustes transformation has several applications in fMRI data analysis including hyper-alignment of voxel-wise responses (Haxby et al, 2011), alignment of macro-anatomical FC gradients (Margulies et al, 2016) and generation of bidirectional mappings between fMRI responses and natural language description of scenes in naturalistic experiments (Vodrahalli et al, 2018). The benefits of using dimensions beyond those being interpreted during alignment has been previously reported for functional gradients by Mckeown et al (2020) who showed that alignment toward a template space significantly improves when using 10 dimensions instead of three [the number of gradients often explored and interpreted in studies that rely on this technique (Margulies et al, 2016;Mckeown et al, 2020;Tian et al, 2020;Hardikar et al, 2022)].…”

Reshaped functional connectivity gradients in acute ischemic stroke

“…Following the first one, the second gradient indicates sensorimotor-visual functions, and the third gradient shows active-passive attention. The gradients of brain function are highly replicable (Bethlehem et al, 2020; Hardikar et al, 2022; S.-J. Hong et al, 2019; Margulies et al, 2016; Mckeown et al, 2020) .…”

“…Gradients of brain function project the complex functional profiles along three orthogonal axes encapsulating most of their variance (Vos de Wael et al, 2020). Importantly, they have been proven to be highly replicable (Bethlehem et al, 2020; Hardikar et al, 2022; Margulies et al, 2016; Mckeown et al, 2020). In healthy cohorts, the major axis splits the cortex into unimodal and multimodal areas, the second axis separates visual and somatomotor functions, and the third gradient distinguishes active-passive attention modalities (Glasser et al, 2016).…”