Age-related changes in the inhibitory: Excitatory balance in macaque monkey primary visual cortex
Bibliographic record
Abstract
Our visual perception changes across the lifespan, reflecting developmental and aging changes in the neural mechanisms that mediate vision. Studies of age-related changes in human visual perception and in macaque monkey physiology have suggested that the main neural mechanism is a loss of inhibition as a result of less GABA. In contrast, a number of recent studies looking at developmental plasticity in V1 have shown that the balance between inhibitory and excitatory mechanisms is a key factor in early maturation. Here, our goal is to characterize age-related changes in excitatory and inhibitory synapses in V1 to determine if changes in aging are specific to GABAergic mechanisms or reflect a change in the inhibitory:excitatory balance. We used Western blot analysis to quantify the expression of 14 synaptic proteins in V1 of macaque monkeys ranging in age from 4–33 years old. We examined the changes in expression of both inhibitory and excitatory synaptic proteins. For inhibition, we measured GABA synthesizing enzymes (GAD65/67), the GABA vesicular transporter (VGAT), the inhibitory modulating cannabinoid receptor (CB1), GABAA receptor subunits (GABAAα1, α2, α3), and the GABA receptor anchoring protein (Gephyrin). For excitation, we measured AMPA receptors (GluR2), NMDA receptor subunits (NR1, NR2A, NR2B), the vesicular glutamate transporter (VGlut), and the excitatory receptor anchoring protein (PSD-95). On the inhibitory side, there was loss of VGAT, Gephyrin, and GABAAα1 with aging. On the excitatory side, there was a loss of PSD-95, NR1, NR2A, and NR2B. To compare the balance, we calculated an index of Gephyrin to PSD-95 expression and found a shift toward more Gephyrin with aging. These results show age-related losses in both inhibitory and excitatory components of synaptic function suggesting that it is a shift in the balance of these mechanisms that underlies visual changes in aging.
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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 | 0.001 | 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) | 0.001 | 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".