Functional Networks in the Anesthetized Rat Brain Revealed by Independent Component Analysis of Resting-State fMRI
Bibliographic record
Abstract
The rodent brain is organized into functional networks that can be studied through examination of synchronized low-frequency spontaneous fluctuations (LFFs) of the functional magnetic resonance imaging -blood-oxygen-level-dependent (BOLD) signal. In this study, resting networks of LFFs were estimated from the whole-brain BOLD signals using independent component analysis (ICA). ICA provides a hypothesis-free technique for determining the functional connectivity map that does not require a priori selection of a seed region. Twenty Long-Evans rats were anesthetized with isoflurane (1%, n = 10) or ketamine/xylazine (50/6 mg . kg(-1) . h(-1) ip, n = 10) and imaged for 5-10 min in a 9.4 T MR scanner without experimental stimulation or task requirement. Independent, synchronous LFFs of BOLD signals were found to exist in clustered, bilaterally symmetric regions of both cortical and subcortical structures, including primary and secondary somatosensory cortices, motor cortices, visual cortices, posterior and anterior cingulate cortices, hippocampi, caudate-putamen, and thalamic and hypothalamic nuclei. The somatosensory and motor cortices typically demonstrated both symmetric and asymmetric components with unique frequency profiles. Similar independent network components were found under isoflurane and ketamine/xylazine anesthesia. The report demonstrates, for the first time, 12 independent resting networks that are bilaterally synchronous in different cortical and subcortical areas of the rat brain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".