Source-linked AI summary
Multiscale Community-Based Fingerprinting of Signed Functional Networks
Sema Athamnah, Selin Aviyente
TL;DR
Existing connectome fingerprints often emphasize edge-level features, leaving higher-order topological organization less interpretable. This paper uses signed multilayer community detection and graph-theoretic community features to construct low-dimensional fingerprints. Across HCP sessions and tasks, the resulting representations are reported as reliable, interpretable, and discriminative, with scalability and community-detection variability remaining limitations.
Problem
Existing fingerprinting methods rely primarily on edge-level connectivity and neglect higher-order network structure, limiting interpretability.
Method
The framework detects subject-specific signed multilayer communities and derives low-dimensional fingerprints from community-level graph-theoretic features.
Results
Community-based fingerprints provide reliable and interpretable individualized representations across sessions and tasks in 810 HCP healthy control subjects.
Takeaways & Limitations
Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints for personalized brain characterization.
Takeaways & Limitations
Modularity maximization does not scale readily to larger cohorts and is affected by degeneracy and stochastic optimization variability.
Abstract
from arXiv · showhide
Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or \textit{fingerprints}, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.
1 Introduction
Functional connectome fingerprints identify individuals, but edge-level approaches provide limited multivariate organization and interpretability. This paper addresses that gap with community-level representations of individualized network structure.
- Functional connectivity patterns are subject-specific and stable across sessions and tasks, supporting individualized brain characterization.
- Existing fingerprinting methods commonly use whole connectivity matrices, subnetworks, embeddings, tensors, or machine-learning features.
- Most existing methods rely on univariate or edge-level features rather than multivariate descriptors of functionally coherent organization.
- The proposed approach shifts fingerprinting from isolated edges to multivariate community-level topological signatures.
- Signed multilayer modularity incorporates positively correlated and anti-correlated interactions when identifying subject-specific communities.
- Discriminative Connectivity Maps connect identification statistics with functional neuroanatomy at ROI or functional-system levels.
- Community-based fingerprints are extracted from multiscale partitions, ranked by mean pairwise Euclidean distance, and used for nearest-neighbor subject identification.
2 Background
The background defines signed functional-connectivity graphs and modularity-based community detection, then extends the setting to coupled multilayer networks. The accompanying figures report within-task fingerprinting across sample sizes and methods.
- Functional connectivity represents pairwise co-fluctuations between brain regions as a weighted, undirected signed graph.
- Signed-network communities favor positive connections within communities and predominantly negative connections between communities.
- Signed modularity optimization assigns each node to exactly one community in a hard, non-overlapping partition.
- The resolution parameter γ controls detected community size, while the signed null model separates positive and negative adjacency contributions.
- Multilayer modularity uses interlayer coupling ω to control consistency between network layers.
- Multiplex networks share the same nodes across layers while allowing their topological structures to differ.
- Figure 2 reports within-task fingerprinting for S = 100, while Figure 3 compares methods at S = 200, 300, and 400.
3 Multiscale Community-Based Functional Connectome Fingerprinting
The framework represents subjects through multiscale community-based fingerprints derived from signed multilayer functional networks. It combines joint community partitions with graph-theoretic community features to characterize individual mesoscale organization.
- 3 Multiscale Community-Based Functional Connectome Fingerprinting: Functional connectivity networks from multiple subjects are modeled as layers of a signed multilayer network.
- 3 Multiscale Community-Based Functional Connectome Fingerprinting: Signed multilayer modularity generates M multiscale partitions containing joint community assignments across subjects.
- 3 Multiscale Community-Based Functional Connectome Fingerprinting: Each subject receives a low-dimensional community-based fingerprint vector for every candidate partition and graph-theoretic feature type.
- 3 Multiscale Community-Based Functional Connectome Fingerprinting: Selected partitions support training and testing fingerprints in a common feature space for direct subject identification.
- 3 Multiscale Community-Based Functional Connectome Fingerprinting: The framework incorporates both correlated and anti-correlated activity while accommodating inter-subject variability in community assignments.
- 3 Multiscale Community-Based Functional Connectome Fingerprinting: Candidate partitions arise from multiple modularity-parameter combinations, revealing joint community structures at distinct topological scales.
3.2 Fingerprint Extraction
Fingerprint extraction converts each community assignment into graph-theoretic descriptors that summarize community roles in signed functional networks. The framework separately measures positive-edge and whole-network organization.
- 3.2 Fingerprint Extraction: For each candidate partition, a fingerprint component records a graph-theoretic feature computed for each community.
- 3.2 Fingerprint Extraction: The framework evaluates participation coefficient and diversity coefficient features independently across candidate partitions.
- 3.2.1 Participation coefficient: Participation coefficient measures global inter-modular integration.
- 3.2.1 Participation coefficient: Positive and total participation coefficients use positive edges alone or the entire signed network, respectively.
- 3.2.2 Diversity coefficient: Diversity coefficient quantifies regional connection diversity using Shannon’s normalized entropy.
- 3.2.2 Diversity coefficient: Positive and total diversity coefficients are computed from positive edges alone or from the entire signed network, respectively.
3.3 Partition Selection
The framework selects multiple highly discriminative partitions rather than relying on a single scale. Selection is based on mean pairwise Euclidean distance across subjects.
- 3.3 Partition Selection: Mean pairwise Euclidean distance is computed across subjects for each feature type and partition.
- 3.3 Partition Selection: Partitions are ranked in descending order by their MPED score.
- 3.3 Partition Selection: The top-L partitions are retained to represent complementary organizational patterns across different topological scales.
- 3.3 Partition Selection: L is the smallest number of ranked partitions attaining at least 99% of the maximum MPED.
3.4 Subject Identification
Subject identification compares testing and training community-based fingerprints constructed from the same selected partitions. A nearest-neighbor rule assigns each testing scan to the closest training representation.
- 3.4 Subject Identification: Selected partitions generate training fingerprints from optimization scans and testing fingerprints from corresponding unseen scans.
- 3.4 Subject Identification: Using the same partitions places training and testing scans in a common feature space for direct comparison.
- 3.4 Subject Identification: Each testing fingerprint is matched against corresponding training fingerprints for its partition and feature type.
- 3.4 Subject Identification: A distance-based nearest-neighbor classifier predicts the subject whose training fingerprint minimizes Euclidean distance to the testing fingerprint.
- 3.4 Subject Identification: Identification success rate is the percentage of correctly identified subjects among all subjects.
3.5 Community-based fingerprint interpretability
The framework ranks communities by reliability and distinctiveness, then maps the most distinctive communities to functional brain systems for interpretation.
- Community distinctiveness: Self-reliability measures how stable a community-based fingerprint is across subjects and sessions using test–retest ICC.Relational similarity averages off-diagonal ICC values to capture overlap or redundancy with other communities.
- Community distinctiveness: Distinctiveness combines high self-reliability with low relational similarity to identify communities that are stable and nonredundant.Communities are ranked in descending order of their distinctiveness scores.
- Spatial interpretation: The highest-ranked communities are selected by identifying their constituent ROIs within the joint community assignment matrix.This links community rankings to the spatial composition of the detected communities.
- Spatial interpretation: Joint community assignments support interpretation across subjects or cohorts and across individual nodes or grouped functional brain systems.The reported analysis focuses on cohort-level, system-level representations.
- Computational considerations: The framework has expected computational complexity O(MSN^2) across M partitions for S subjects and N nodes.Signed multilayer Louvain optimization costs O(SN^2) per partition.
4 Experimental Results
Across within-task and between-task evaluations, community-based fingerprints were tested on HCP resting-state and seven task-fMRI conditions, with CBF-PC+ generally strongest and robust across tasks and cohort sizes.
- Experimental design: The evaluation covered within-task and between-task fingerprinting across eight HCP fMRI conditions, including resting state and seven tasks.The included tasks were emotion, gambling, language, motor, relational, social, and working memory processing.
- Experimental design: The study compared CBF variants with Corr-FC, GEFF, SVM, and neural-network fingerprinting methods.The proposed variants were CBF-PC+, CBF-PC, CBF-DC+, and CBF-DC.
- Small-scale evaluation: Ten independent subject groups of S=100 were analyzed to assess robustness to subject selection across feature types and tasks.Repeating the pipeline across randomly selected groups reduced dependence on a particular subject group.
- Small-scale evaluation: CBF-PC+ achieved the highest identification accuracy among proposed features across tasks and outperformed competing methods for most tasks, although NN was slightly better for REST1 and LAN.CBF-PC followed CBF-PC+, while CBF-DC+ and CBF-DC remained competitive.
- Large-scale evaluation: As sample size increased to S=200, 300, and 400, accuracy decreased, but CBF-PC+ maintained relatively high performance across tasks and cohort sizes.Baseline methods were more sensitive to sample size and task condition, particularly on task-based fMRI.
- Large-scale evaluation: The three-way ANOVA found significant effects of task, sample size, and method, with CBF-PC+ significantly outperforming all competing methods at adjusted p<0.05.Significant interactions also occurred among method, sample size, and fMRI task.
- Between-task evaluation: CBF-PC+ produced the strongest cross-task performance at S=400, with the highest off-diagonal accuracies across nearly all task pairs.Other community-based features also outperformed competing approaches, whose cross-task generalization was lower.
5 Discussion
The paper argues that mesoscale community structure provides a more interpretable basis for subject-specific fingerprinting than edge-level connectivity, with task-specific discriminative patterns distributed across functional systems. Across tasks, dominant systems vary, while higher-order association networks recur and top-ranked spatial signatures remain consistent when expanded from the top 1% to top 5% of communities.
- Motivation and contribution: Community-based fingerprints address the limited interpretability of edge-level methods by representing individual differences in mesoscale network organization.The approach captures functionally meaningful subgraphs rather than isolated connections.
- Identification performance: The proposed method outperforms state-of-the-art approaches across both within-task and between-task identification settings.The comparison used one scan per subject under the same experimental conditions for all methods.
- Task-specific organization: Higher-order association networks, including DMN, FPN, and DAN, consistently contribute across tasks, indicating that subject variability is encoded within large-scale integrative networks.The fingerprints are neither globally uniform nor confined to a single functional network.
- Robustness of spatial patterns: Top-1% and top-5% discriminative connectivity maps show high spatial consistency across all eight tasks.Additional highly discriminative communities refine the fingerprint without fundamentally changing its task-specific organization.
- Limitations and future work: The principal limitation is the scalability of modularity maximization for larger subject cohorts, compounded by stochastic variability in community detection.Future work proposes parallel implementations and alternative optimization strategies.
6 Conclusion
The paper presents brain fingerprinting through individual differences in mesoscale functional-network organization. A signed multilayer community framework produces stable, interpretable, individualized representations across tasks and sessions, supporting precision neuroimaging applications.
- Conclusion: The framework represents each subject by community-based fingerprints that capture mesoscale organization across fMRI tasks and sessions.It incorporates correlations and anticorrelations to identify subject-specific communities and derives graph-theoretic fingerprints.
7 Compliance with Ethical Standards
The study used retrospectively analyzed open-access human subject data under the attached data license. Ethical approval was not required according to that license.
- Compliance with Ethical Standards: The retrospective study used open-access human subject data, and the attached license indicated that ethical approval was not required.