Plasticity of inhibitory networks in neuropsychiatric disorders: Froma animal models to patients
Bibliographic record
Abstract
The central nervous system (CNS) is complex, regarding its the cellular and network diversity; and plastic, meaning its connections are dynamic and adaptative. The CNS plasticity is especially increased during the critical periods, during the childhood and early adolescence, resulting in a vulnerability of the brain to adverse experiences during these periods. Early life stress (ELS) has a strong impact in the inhibitory networks of the brain, specifically in the parvalbumin (PV) expressing neurons, which are fast-spiking inhibitory neurons that can modulate the activity of brain networks. These cells are closely associated with plasticity-related molecules, such as the polysialylated form of the neural cell adhesion molecule (PSA-NCAM) and a specialized form of the extracellular matrix: the perineuronal nets (PNNs). While PSA-NCAM increases brain plasticity, having a peak of expression during early brain development, PNNs contribute to the closure of critical periods, reaching their peak concentration in the later stages of neural maturation. Inhibitory networks and PV+ cells are also impaired in psychiatric disorders such as major depression (MD), schizophrenia (SZCH) and bipolar disorder (BD). In this context of brain plasticity and inhibitory transmission I wanted to focused my studies on the effects of early stress (ELS) and different psychiatric disorders on the inhibitory neurons of 2 brain regions: the prefrontal cortex (PFC), a canonical area for the study of the effects of stress, in which inhibitory neurons and their plasticity are impaired; and the thalamic reticular nucleus (TRN), a thalamic nucleus entirely composed by inhibitory neurons that acts as a relay between the cortex and the thalamus. In order to do so I followed two different strategies; first I subjected female and male mice to a peripubertal stress model (PPS) and analyzed its impact in adult brain. With this experiment I found that PPS model disrupts plasticity and functional regulators of PV+ neurons specifically in female mice. Secondly, I analyzed postmortem brains from two different cohorts, one from the Stanley Medical Research Institute, containing patients diagnosed with MD, BD, or SCHZ, along with control subjects, and other from the Douglas-Bell Canada Brain Bank, consisting in control subjects, MD patients and MD patients who had suffered child abuse. Using these cohorts I found that in the dorsolateral PFC 75 % of PV+ cells were surrounded by PNNs and that patients with a history of psychotic episodes exhibited a lower PNN density in this region. When analyzing the TRN, I found that 1.42% of PVALB+ cells were inhibitory, the 72.2 % of which were surrounded by PNNs. I also found a significant effect of the disorders in the PNNs and PV+ cells, as well as in microglial cells. Interestingly, some of these changes were specific of MD patients who had not suffered child abuse but were not present in those with a story of child abuse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".