The laminar pattern of resting state in human cerebral cortex
Bibliographic record
Abstract
Resting state functional Magnetic Resonance Imaging (RS-fMRI) provides the means to measure neuronal activity. One of the most commonly used methods to explore the RS-fMRI signal is the Probabilistic Independent Component Analysis (PICA). PICA allows to depict brain functional connectivity (FC) networks. Yet most of the IC maps obtained with this method do not represent any particular FC network. Consequently, those IC maps are classified as artifacts or noise of an unknown source. We hypothesized that the unexplained RS-fMRI signal patterns that are picked up by the PICA can be related to the differences in oxygen metabolism and blood flow in cortical layers. This study aimed at (1) providing preliminary evidence to the effects of laminar organization of neocortex on the RS-fMRI signal, and (2) evaluating the application of laminar maps to aid the classification of IC maps. We created laminar maps 1-4 that depict relative cortical thickness of layers IV and VI. Our data show that the RS-fMRI signal is significantly related to the relative thickness of the cortical layer VI but not layer IV. Importantly, the laminar maps 1-4 overlap with four separate IC maps. Thus, the laminar maps 1-4 improve classification and interpretation of the IC maps. Moreover, the laminar maps 1-4 may be considered as FC networks that are the bridging piece between particular cognitive functions. Together, these data provide preliminary evidence to the fundamental questions about the role of cortical layering in the RS-fMRI signal and brain FC networks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".