Plasticity of inhibitory networks in neuropsychiatric disorders: Froma animal models to patients
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».