Long-Term<i>In Vivo</i>Imaging of Dendritic Spines in the Hippocampus Reveals Structural Plasticity
Bibliographic record
Abstract
Hippocampal function is important for learning and memory. During memory processing, hippocampal CA1 neurons play a crucial role by integrating excitatory synaptic input from CA3 and the entorhinal cortex. These neurons receive excitatory input almost exclusively on dendritic spines. The formation and elimination--structural plasticity--of dendritic spines reflect wiring changes within the hippocampal network. Despite the relevance of the hippocampus in learning and memory, most in vivo data on structural plasticity derive from cortical regions. We established a chronic hippocampal window approach using two-photon microscopy to visualize dendritic spines throughout all CA1 hippocampal layers and over a time course of weeks. Moreover, even granule cells in dentate gyrus could be reliably detected. We found that the spine density in stratum radiatum (∼1.1 per micrometer) remained stable over weeks. However, a small fraction (3.4%) of spines were formed and eliminated between imaging sessions, which demonstrated that spines of CA1 neurons exhibit structural plasticity in adult mice. In addition, we tested for possible inflammatory or behavioral side effects of hippocampal window implantation. Mice exhibited a transient increase in microgliosis and astrogliosis, which declined within a few weeks. We did not detect any difference in behavioral performance in an open-field and contextual fear-conditioning paradigm. In conclusion, hippocampal long-term two-photon imaging revealed structural plasticity of dendritic spines in CA1 pyramidal neurons. This approach may provide a powerful tool to analyze changes in neuronal network rewiring during hippocampal learning and memory processes in health and disease.
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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 | 0.001 |
| 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".