Efficacy of functional connectome fingerprinting using tangent-space brain networks
Bibliographic record
Abstract
ABSTRACT Functional connectomes (FCs) are estimations of brain region interaction derived from brain activity, often obtained from functional Magnetic Resonance Imaging recordings. Quantifying the distance between FCs is important for understanding the relation between behaviour, disorders, disease, and changes in connectivity. Recently, tangent space projections, which account for the curvature of the mathematical space of FCs, have been proposed for calculating FC distances. We compare the efficacy of this approach relative to the traditional method in the context of subject identification using the Midnight Scan Club dataset, in order to study resting-state and task-based subject discriminability. The tangent space method is found to universally out-perform the traditional method. We also focus on the subject identification efficacy of subnetworks. Certain subnetworks are found to outperform others, a dichotomy which largely follows the ‘control’ and ‘processing’ categorization of resting state networks, and relates subnetwork flexibility with subject discriminability. Identification efficacy is also modulated by tasks, though certain subnetworks appear task independent. The uniquely long recordings of the dataset also allow for explorations of resource requirements for effective subject identification. The tangent space method is found to universally require less data, making it well suited when only short recordings are available. AUTHOR SUMMARY Functional connectomes, which describe the similarity between the recorded activity of different brain regions, are ubiquitous for researcher trying to understand brain dynamics on short and long time scales in the presence and absence of neurophysiological diseases. This work applies a Tangent Space approach, a novel method to calculate the distance between functional connectomes, to a unique high-quality dataset (the Midnight Scan Club) in order to better understand the variability and uniqueness of connectomes across subjects, and how subject identification (also called fingerprinting) compares across tasks. We also show that not only does the tangent space method offer greater sensitivity to changes, it does so with significantly fewer resources, and in some scenarios reveals qualitatively different interpretations than the traditional method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".