Brain Structure and FunctionNeurosciencePeer-reviewed journal article
Comparing Intra- and Inter-individual Correlational Brain Connectivity from Functional and Structural Neuroimaging Data
Xin DiiD, Bharat B. BiswaliD, Alzheimer’s Disease Neuroimaging Initiative
Abstract
Inferring brain connectivity from inter-individual correlations has been applied across various neuroimaging modalities, including positron emission tomography (PET) and MRI. The variability underlying these inter-individual correlations is generally attributed to factors such as genetics, life experiences, and long-term influences like aging. This study leveraged two unique longitudinal datasets to examine intra-individual correlations of structural and functional brain measures across an extended time span. By focusing on intra-individual correlations, we aimed to minimize individual differences and investigate how aging and state-like effects contribute to brain connectivity patterns. Additionally, we compared intra-individual correlations with inter-individual correlations to better understand their relationship. In the first dataset, which included repeated scans from a single individual over 15 years, we found that intra-individual correlations in both regional homogeneity (ReHo) during resting-state and gray matter volumes (GMV) from structural MRI closely resembled resting-state functional connectivity. However, ReHo correlations were primarily driven by state-like variability, whereas GMV correlations were mainly influenced by aging. The second dataset, comprising multiple participants with longitudinal Fludeoxyglucose (18F) FDG-PET and MRI scans, replicated these findings. Both intra- and inter-individual correlations were strongly associated with resting-state functional connectivity, with functional measures (i.e., ReHo and FDG-PET) exhibiting greater similarity to resting-state connectivity than structural measures. This study demonstrated that controlling for various factors can enhance the interpretability of brain correlation structures. While inter- and intra-individual correlation patterns showed similarities, accounting for additional variables may improve our understanding of inter-individual connectivity measures.
- organism: human
- assay: bulk RNA-seq
- assay: imaging
- research focus: benchmarking
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- approach: experimental assay
- Citations
- 3
- FWCI
- 1.71
- Field percentile
- top 17%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Effects of Tasks on Functional Brain Connectivity Derived from Inter-Individual Correlations: Insights from Regional Homogeneity of Functional MRI Data
Xin Di, Pratik Jain, Bharat B. Biswal·Brain ConnectivityPeer-reviewed·Jun 26, 2025
- Concordance Among Indices of Intrinsic Brain Function: Insights from Inter-Individual Variation and Temporal Dynamic
Chao‐Gan Yan, Zhen Yang, … Michael Peter Milham·中国科学通报:英文版Peer-reviewed·Oct 18, 2017
- How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics
Lukas Roell, Stephan Wunderlich, … Daniel Keeser·NeuroImagePeer-reviewed·Apr 9, 2025
- Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations
Xin Di, Ting Xu, … Bharat B. Biswal·Developmental Cognitive NeurosciencePeer-reviewed·Jul 17, 2023
- Individual Variability in the Structural Connectivity Architecture of the Human Brain
Weijie Huang, Haojie Chen, … Ni Shu·Journal of NeurosciencePeer-reviewed·Dec 12, 2024
- Investigating the effects of healthy cognitive aging on brain functional connectivity using 4.7 T resting-state functional Magnetic Resonance Imaging.
Stanislau Hrybouski, Ivor Cribben, … Nikolai V. Malykhin·Brain Structure and FunctionPeer-reviewed·Feb 18, 2021