Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Kong, Ru; Tan, Yan Zhi; Wulan, Naren; Ooi, Leon Qi Rong; Farahibozorg, Seyedeh-Rezvan; Harrison, Sarah M.; Bijsterbosch, Janine; Bernhardt, Boris C.; Eickhoff, Simon B.; Yeo, B.T. Thomas

doi:10.1101/2022.09.22.509045

Cited by 8 publications

(12 citation statements)

References 85 publications

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“…In fact, parcellations of different resolutions are likely useful for different applications. For example, behavioral prediction generally improves with higher dimensional parcellations before plateauing or becoming worse, although the exact results can be quite variable across studies (Dadi et al, 2019; Pervaiz et al, 2020; Kong et al, 2022). On the other hand, a recent study has suggested that brain–behavior relationships are scale-dependent (Betzel et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Yan

Kong

Xue

et al. 2022

Preprint

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Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local- global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (GITHUB_LINK).

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Section: Discussionmentioning

confidence: 99%

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Yan

Kong

Xue

et al. 2022

Preprint

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“…Recent work has recognized the need for massive numbers of subjects to reliably associate brain phenotypes with behavior, specifically in developmental RSFC (Marek et al, 2022). This could be attributed to the complexity of neuroimaging phenotypes, but by leveraging machine learning advances like multivariate models, functional alignment, and individualized atlases, we can improve prediction (Dubois and Adolphs, 2016; Kong et al, 2019, 2021, 2023; Rosenberg et al, 2020; Feilong et al, 2021, 2022; Chen et al, 2022; DeYoung et al, 2022). We demonstrate hyperalignment’s power as a tool for understanding developing brain–behavior associations.…”

Section: Discussionmentioning

confidence: 99%

“…While computing connectivity profiles to be hyperaligned, it is often desirable to use individualized connectivity targets to account for topographic idiosyncrasies in target regions. Individualized connectivity targets can be generated with individualized parcellations (Glasser et al, 2016; Langen et al, 2018; Kong et al, 2019, 2021, 2023; Anderson et al, 2021) or by iterating the hyperalignment algorithm (Busch et al, 2021; Jiahui et al, 2023). In our implementation for this study, we used parcels from the Glasser cortical parcellation (Glasser et al, 2016) as the cortical fields and targets to be hyperaligned.…”

Section: Methodsmentioning

confidence: 99%

“…Parcellations assume the same regional boundaries exist across brains, regardless of individual or developmental differences (Bandettini et al, 2022). Individual– defined parcellations better capture individualized functional topography (Glasser et al, 2016; Kong et al, 2019, 2021, 2023), yet cover large surface areas and collapse over local, fine-scale information processing. A useful alternative, functional alignment maximizes inter-subject correspondence while resolving fine-scale anatomical variation.…”

Section: Introductionmentioning

confidence: 99%

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

Busch

Rapuano

Anderson

et al. 2022

Preprint

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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.

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“…Others have identified changes in gradient range in response to changing states or task demands (Brown et al, 2022; Cross et al, 2021; Gale et al, 2022; Murphy et al, 2019; Shao et al, 2022; Zhang et al, 2022). Still others have leveraged regional values from one or more FC gradients to predict various behavioral features in multivariate analyses (Bethlehem et al, 2020; Hong et al, 2020; Kong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Test–retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Knodt,

Elliott,

Whitman

et al. 2023

Human Brain Mapping

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Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain‐wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory‐to‐association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test–retest data from the young adult Human Connectome Project (HCP‐YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge‐wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting‐state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP‐aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP‐YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge‐wise FC measures in the search for brain–behavior associations.

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Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

Cited by 8 publications

References 85 publications

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

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

Test–retest reliability and predictive utility of a macroscale principal functional connectivity gradient

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