Topological properties of modular networks, with a focus on networks of functional connections in the human brain
Bibliographic record
Abstract
Complex network theory offers useful approaches to analyze the structural and functional properties of real life networks. In this work, we explore some of the mathematical concepts of network theory and study real life systems from a complex network perspective. We pay particular attention to networks of connections within the human brain. We analyze weighted networks calculated from full functional magnetic resonance imaging (fMRI) data acquired during task performance. The first novelty of this study is the fact that we retain all of the connections between all of the voxels in the full brain fMRI data. We then evaluate the extent to which this rich dataset can be described by existing models of scale-free or exponentially truncated scale-free topology, comparing results across a large number of more complex topological models as well. Our results suggest that the novel model presented in this dissertation offers a significantly better fit at the voxel level.\nStructural characterization of the brain can also give insights into the effects of traumatic injuries. For our second study, we used brain networks to explore the topological consequences of brain damage in computational models. By simulating the effects of several traumatic brain injury (TBI) models we find a variety of disruptions in the modular structure and connectivity of the post-injury networks. In particular, we focused on the effects of focalized injuries, axonal degeneration and diffuse microlesions. Our results suggest that mathematical models can predict, to some extent, the structural and functional effects of TBIs based on the analysis of specific topological measures. Furthermore, these results may be correlated to known cognitive sequelae of TBI. In order to complement the set of topological properties studied, we review the concept of participation of a node in a modular network. The participation coefficient is often used to provide a ranking of the "importance" of a node; however, we propose a new measure, called the gateway coefficient, to assess the involvement of a node. Our results suggest that the gateway coefficient has a superior ranking power than the participation coefficient since it takes into account a variety of modular properties of the network. These results are illustrated with examples of simulations and real life datasets, such as the air transportation network and the human brain network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".