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

Busch, Erica L.; Rapuano, Kristina M.; Anderson, Kevin Michael; Rosenberg, Monica D.; Watts, Richard; Casey, B. J.; Haxby, James V.; Feilong, Ma

doi:10.1101/2022.05.24.493295

Cited by 3 publications

(3 citation statements)

References 108 publications

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“…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 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…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.…”

Section: Discussionmentioning

confidence: 80%

Similarity in Functional Connectome Architecture Predicts Teenage Grit

Park

Kim

2023

Preprint

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Grit is a personality trait that encapsulates the tendency to persevere and maintain consistent interest for long-term goals. While prior studies found that grit predicts positive behavioral outcomes, there is a paucity of work providing explanatory evidence from a neurodevelopmental perspective. Based on previous research suggesting the utility of the functional connectome as a developmental measure, we tested the idea that individual differences in grit might be, in part, rooted in brain development in adolescence and emerging adulthood (N = 64, 11-19 years of age). Our analysis showed that grit was associated with connectome stability across conditions and connectome similarity across individuals. Notably, inter-subject representational similarity analysis revealed that teenagers who were grittier shared similar functional connectome architecture with each other, more so than those with lower grit. Our findings suggest that gritty individuals are more likely to follow a specific neurodevelopmental trajectory, which may underpin subsequent beneficial behavioral outcomes.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Steel

Garcia

Silson

et al. 2022

Preprint

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fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance on data acquired during a visual population receptive field (pRF) mapping paradigm (N=13 participants) after using one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyses and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including task fMRI studies, voxel-wise modeling, and naturalistic paradigms.

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

Cited by 3 publications

References 108 publications

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

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

Similarity in Functional Connectome Architecture Predicts Teenage Grit

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

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