ArticleNeuroImage2023
Comparison between gradients and parcellations for functional connectivity prediction of behavior.
Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- A cortical gradient in area 55b links language and somato-cognitive action networks.Research square · 2026Article
- From topography to connectome: Towards an integrated understanding of the resting brain.bioRxiv : the preprint server for biology · 2026Article
- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 2026Review
- Bridging glucose metabolism and intrinsic functional organization of the human cortex.Communications biology · 2026Article
- Divergence unveils further distinct phenotypic traits of human brain connectomics fingerprint.iScience · 2026Article
- Individual identifiability following Procrustes alignment of functional gradients: effect of subspace dimensionality.Communications biology · 2026Article
- The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.Network neuroscience (Cambridge, Mass.) · 2026Article
- Article
- Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Wavelet Analysis of Dual-fMRI-Hyperscanning Reveals Cooperation and Communication Dependent Effects on Interbrain Neuronal Coherence.Human brain mapping · 2025Article
- Article
- Development of areal-level individualized homologous functional parcellations in youth.Communications biology · 2025Article
- The generalizability of cortical area parcellations across early childhood.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- MASKED MOMENTUM CONTRASTIVE DYNAMIC TRANSFORMER FOR SELF-SUPERVISED FUNCTIONAL CONNECTIVITY REPRESENTATION LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Improving brain difference identification in autism spectrum disorder through enhanced head motion correction in ICA-AROMA.Communications biology · 2025Article
- Functional differentiation of the default and frontoparietal control networks predicts individual differences in creative achievement: evidence from macroscale cortical gradients.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Using precision approaches to improve brain-behavior prediction.Trends in cognitive sciences · 2025Review
- A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.Scientific reports · 2025Article
- From gradients to cognition: linking cortical manifolds to brain flexibility and disorder.Frontiers in cognition · 2025Review
- The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.
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