Biology subjects

Corruble, E.

Publications and source records attributed to Corruble, E..

3 recordsLinked to original sources

bioRxiv2025

Neural correlates in basolateral and central amygdala during reward seeking in the face of punishment

The inability to suppress actions despite adverse consequences is a hallmark of compulsive behaviors and is amygdala dependent. To study the amygdalas role in responding despite adverse consequences, we compared single-unit activity in the basolateral (BLA) or central (CeA) amygdala before and after reward-seeking under punishment threat. Rats started each trial by pressing an initial lever, which triggered an outcome-specific 5-s auditory cue (white noise, pure tone, or clicker), signaling one of three reinforcement conditions: 100% sucrose reward, 20% reward/80% omission, or 80% reward/20% footshock punishment. Next, the active and inactive levers were extended, and pressing on the active lever terminated the auditory cue and triggered an outcome-specific 1-s visual cue predicting either reward, reward omission, or shock. After training, we implanted eight tetrodes in the BLA (n=7) or CeA (n=5) and recorded single-unit activity of [~]100 neurons per region during task performance. CeA neurons, and to a lesser extent BLA neurons, responded differently to the distinct auditory cues and outcomes. The discrimination between conditions partially explained the capacity of neuronal activity to predict the latency to lever press to complete the trial. The failure to suppress reward-seeking behavior in the face of punishment coincided with the reactivation of reward-seeking-sensitive and the loss of inhibition of punishment-sensitive neuronal populations. In conclusion, we found that after extended training, opposing populations of activated and inhibited neurons in CeA, and to a lesser extent in BLA, control completion of reward-seeking despite punishment.

neuroscience↗

bioRxiv2024

Ketamine metabolism via hepatic CYP450 isoforms contributes to its sustained antidepressant actions.

(R,S)-ketamine (ketamine) has rapid and sustained antidepressant (AD) efficacy at sub-anesthetic doses in depressed patients. A metabolite of ketamine, including (2R,6R)-hydroxynorketamine ((6)-HNKs) has been reported to exert antidepressant actions in rodent model of anxiety/depression. To further understand the specific role of ketamines metabolism in the AD actions of the drug, we evaluated the effects of inhibiting hepatic cytochrome P450 enzymes on AD responses. We assessed whether pre-treatment with fluconazole (10 and 20 mg/kg, i.p.) 1 hour prior to ketamine or HNKs (10 mg/kg, i.p.) administration would alter behavioral and neurochemical actions of the drugs in male BALB/cJ mice with a highly anxious phenotype. Extracellular microdialysate levels of glutamate and GABA (Gluext, GABAext) were also measured in the medial prefrontal cortex (mPFC). Pre-treatment with fluconazole altered the pharmacokinetic profile of ketamine, by increasing both plasma and brain levels of ketamine and (R,S)-norketamine, while robustly reducing those of (6)-HNKs. At 24 hours post-injection (t24h), fluconazole prevented the sustained AD-like response of ketamine responses in the forced swim test and splash test, as well as the enhanced cortical GABA levels produced by ketamine. A single (2R,6R)-HNK administration selectively rescued the antidepressant-like activity of ketamine in mice pretreated with fluconazole within 24 hours of treatment. Overall, these findings are consistent with an essential role of (6)-HNK in mediating the sustained antidepressant-like effects of ketamine and suggest potential interactions between pharmacological CYPIs and ketamine during antidepressant treatment in patients.

neuroscience↗

bioRxiv2022

A Large-Scale ENIGMA Multisite Replication Study of Brain Age in Depression

BackgroundSeveral studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived "brain age gap" has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N=2,126 controls & N=2,675 cases; +1.08 years [SE 0.22], Cohens d=0.14, 95% CI: 0.08-0.20), in independent cohorts that were not part of the original study. MethodsA previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scanners. ResultsOur ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2=0.47, MAE=7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohens d{square}={square}{square}.15, 95% CI: 0.05-0.25) compared with controls, highly similar to our previous finding. ConclusionsThis study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3,400 patients and >2,800 controls worldwide show reliable but subtle effects of brain aging in adult depression.

neuroscience↗