Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development

Abstract

Decades of human magnetic resonance imaging (MRI) research demonstrate that variance in neuroimaging phenotypes, including functional connectivity, relate to genetics1–5 and predict cognitive traits6–9. The functional connectome affords information transmission through the brain at various spatial scales, from global oscillations between broad cortical regions to fine-scale connections that underlie specific information processing10,11. In adults, while both the coarse- and fine-scale functional connectomes predict cognition6,12–14, the fine-scale connectome predicts twice as much cognitive variance15. Yet, past brain-wide association studies, particularly using large developmental samples, have limited their focus to the coarse connectome to understand the neural underpinnings of individual differences in cognition8,9,16–18. We studied resting-state fMRI in 1,115 children (including 389 twin pairs) and used functional alignment to afford access to individual differences in the fine-scale connectome10,19,20. We found that even though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale connectome, they are less heritable. This surprising result indicates that genetically-determined versus experience-dependent factors in brain development have dissociable effects on these two spatial scales of the connectome. We show further that both connectome scales equally predict a more heritable trait (general cognitive ability) in childhood, but only the fine scale effectively predicts a more experience-driven trait (learning/memory). As such, the developing functional connectome resembles a LEGOⓇ set: the specific pieces a child has parameterizes what they will eventually build, but even when given identical sets, two children with unique experiences will build different creations.

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

“…Functional MRI data is powerful tool for investigating neural activity in the human brain, providing a window into brain organization ( Bassett and Sporns, 2017 ;Buckner et al, 2011 ;Bullmore and Sporns, 2012 ;Busch et al, 2022 ;Feilong et al, 2021 ;Gomez et al, 2019a ;Gordon et al, 2017 ;Gratton et al, 2018 ;Huntenburg et al, 2018 ;Kanwisher et al, 1997 ;Margulies et al, 2016 ;Murphy et al, 2018 ;Thomas Yeo et al, 2011 ) and neural computations ( Baldassano et al, 2017 ;Caucheteux and King, 2022 ;Constantinescu et al, 2016 ;Doeller et al, 2010 ;Güçlü and van Gerven, 2015 ;Hasson et al, 2008 ;Huth et al, 2016 ;Kriegeskorte et al, 2008 ;Lescroart and Gallant, 2019 ;Popham et al, 2021 ;Sha et al, 2015 ). However, the contribution of non-neuronal noise, such as motion, heart rate, respiration, and hardware-related artifacts, severely impacts the quality of fMRI data ( Bright and Murphy, 2017 ;Caballero-Gaudes and Reynolds, 2017 ;Friston et al, 1996 ;Liu, 2016 ).…”

Similarity in Functional Connectome Architecture Predicts Teenage Grit

“…Of relevance, recent studies demonstrated that FC showed greater reliability than individual edges, which implies that it may not be the simple sum of its components (Pannunzi et al, 2017; Noble et al, 2017). While it is still a possibility that specific grit-related functional networks could be revealed when using methods such as fine-scale FC (Busch et al, 2022), our findings highlight the shared whole-brain neural representations among gritty teenagers encouraging future research to examine functional systems across the brain.…”

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

“…Functional MRI data is powerful tool for investigating neural activity in the human brain, providing a window into brain organization (Avena-Koenigsberger et al, 2017; Bassett and Bullmore, 2006, 2017; Bassett and Sporns, 2017; Buckner et al, 2011; Bullmore and Sporns, 2012; Busch et al, 2022; Feilong et al, 2021; Gomez et al, 2019a; Gordon et al, 2017; Gratton et al, 2018; Grill-Spector and Weiner, 2014; Huntenburg et al, 2018; Kanwisher et al, 1997; Laumann et al, 2015; Margulies et al, 2016; Murphy et al, 2018; Power et al, 2011; Thomas Yeo et al, 2011) and neural computations (Allen et al, 2021; Baldassano et al, 2017; Breedlove et al, 2020; Caucheteux and King, 2022; Chang et al, 2021; Constantinescu et al, 2016; Doeller et al, 2010; Gomez et al, 2019a; Güçlü and van Gerven, 2015; Hasson et al, 2008; Honey et al, 2012; Huth et al, 2016, 2012; Kay et al, 2015a; Kriegeskorte et al, 2008; Lescroart and Gallant, 2019; Popham et al, 2021; Sha et al, 2015; Wager et al, 2013). However, the contribution of nonneuronal noise, such as motion, heart rate, respiration, and hardware-related artifacts, severely impacts the quality of fMRI data (Bright and Murphy, 2017; Caballero-Gaudes and Reynolds, 2017; Friston et al, 1996; Liu, 2016).…”