Spontaneous Neural Network Oscillations in Hippocampus, Cortex, and Locus Coeruleus of Newborn Rat and Piglet Brain Slices
Bibliographic record
Abstract
Rises of cytosolic Ca2+ (Cai) associated with early network oscillations (ENOs) are important for brain maturation. Thus, developing neural networks are often studied by combining Cai imaging with electrophysiological recording of extracellular activity and/or intracellular “patch-clamp” analysis. At birth, some nervous systems such as medullary respiratory networks are functional while cortical circuits are yet quite immature. Here, we summarize our experimental approaches to investigate how both mature and developing neuron-glia networks in newborns generate spontaneous synchronized bursting and how such activity is modulated by (pharmacological) experimental manipulation mimicking neurological diseases or their treatment. For this, we studied ENOs in cortex and hippocampus of newborn rat and piglet brain slices, whereas ENO-like bursting in locus coeruleus was only analyzed in rat slices. All these activities are stable for several hours in superfusate of close-to-physiological ion content. Similar to isolated inspiratory network bursting, ENOs depend on a “Ca2+/K+ antagonism” meaning that depressed bursting in elevated superfusate Ca2+ is countered by raised K+. As further example for our findings, anoxia abolishes ENOs and bursting in locus coeruleus, whereas μ-opioid receptor activation blocks bursting, transforms burst pattern, or has no clear effect in locus coeruleus, hippocampus, and cortex, respectively. Multiphoton Cai imaging reveals different responses to neuromodulators in neurons versus neighboring astrocytic glia which forms the basis for their further discrimination via morphological fluorescence imaging of sulforhodamine-101 or glial acidic fibrillary protein. Our findings indicate that “electrophysiological imaging” in brain slices from neonatal mammals is a potent tool for studying spontaneously active (developing) central neuron-glia networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".