The Impact of Normalization and Segmentation on Resting-State Brain Networks
Bibliographic record
Abstract
Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. The authors used graph theory analysis applied to resting-state functional magnetic resonance imaging to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented Montreal National Institute (MNI) 152 template, using the Destrieux and subcortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties, and on the network properties levels. The results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".