Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli.

The default mode network (DMN) is critical for self-referential mental processes, and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber photometry with functional magnetic resonance imaging (fMRI) and computational modeling to characterize dynamics of putative rat DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. Our analysis revealed neuronal activity changes in AI and DMN nodes preceding fMRI-derived DMN activations and cyclical transitions between brain network states. Furthermore, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate the neurophysiological foundations of the rodent DMN, its spatiotemporal dynamical properties, and modulation by salient stimuli, paving the way for future translational studies.

Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net.

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.

Normal Aging does Not Impair Orbitofrontal-Dependent Reinforcer Devaluation Effects.

Normal aging is associated with deficits in cognitive flexibility thought to depend on prefrontal regions such as the orbitofrontal cortex (OFC). Here, we used Pavlovian reinforcer devaluation to test whether normal aging might also affect the ability to use outcome expectancies to guide appropriate behavioral responding, which is also known to depend on the OFC. Both young and aged rats were trained to associate a 10-s conditioned stimulus (CS+) with delivery of a sucrose pellet. After training, half of the rats in each age group received the sucrose pellets paired with illness induced by LiCl injections; the remaining rats received sucrose and illness explicitly unpaired. Subsequently, responding to the CS+ was assessed in an extinction probe test. Although aged rats displayed lower responding levels overall, both young and aged rats conditioned to the CS+ and developed a conditioned taste aversion following reinforcer devaluation. Furthermore, during the extinction probe test, both young and aged rats spontaneously attenuated conditioned responding to the cue as a result of reinforcer devaluation. These data show that normal aging does not affect the ability to use expected outcome value to appropriately guide Pavlovian responding. This result indicates that deficits in cognitive flexibility are dissociable from other known functions of prefrontal - and particularly orbitofrontal - cortex.