Time-course and mechanisms of homeostatic plasticity in layers 2/3 and 5 of the barrel cortex

Recent studies have shown that ocular dominance plasticity in layer 2/3 of the visual cortex exhibits a form of homeostatic plasticity that is related to synaptic scaling and depends on TNFα. In this study, we tested whether a similar form of plasticity was present in layer 2/3 of the barrel cortex and, therefore, whether the mechanism was likely to be a general property of cortical neurons. We found that whisker deprivation could induce homeostatic plasticity in layer 2/3 of barrel cortex, but not in a mouse strain lacking synaptic scaling. The time-course of homeostatic plasticity in layer 2/3 was similar to that of L5 regular spiking (RS) neurons (L5RS), but slower than that of L5 intrinsic bursting (IB) neurons (L5IB). In layer 5, the strength of evoked whisker responses and ex vivo miniature excitatory post-synaptic currents (mEPSCs) amplitudes showed an identical time-course for homeostatic plasticity, implying that plasticity at excitatory synapses contacting layer 5 neurons is sufficient to explain the changes in evoked responses. Spontaneous firing rate also showed homeostatic behaviour for L5IB cells, but was absent for L5RS cells over the time-course studied. Spontaneous firing rate homeostasis was found to be independent of evoked response homeostasis suggesting that the two depend on different mechanisms

Epileptiform activity in the mouse visual cortex interferes with cortical processing in connected areas

Epileptiform activity is associated with impairment of brain function even in absence of seizures, as demonstrated by failures in various testing paradigm in presence of hypersynchronous interictal spikes (ISs). Clinical evidence suggests that cognitive deficits might be directly caused by the anomalous activity rather than by its underlying etiology. Indeed, we seek to understand whether ISs interfere with neuronal processing in connected areas not directly participating in the hypersynchronous activity in an acute model of epilepsy. Here we cause focal ISs in the visual cortex of anesthetized mice and we determine that, even if ISs do not invade the opposite hemisphere, the local field potential is subtly disrupted with a modulation of firing probability imposed by the contralateral IS activity. Finally, we find that visual processing is altered depending on the temporal relationship between ISs and stimulus presentation. We conclude that focal ISs interact with normal cortical dynamics far from the epileptic focus, disrupting endogenous oscillatory rhythms and affecting information processing

Altered neocortical dynamics in a mouse model of Williams-Beuren syndrome

Williams-Beuren syndrome (WBS) is a rare neurodevelopmental disorder characterized by moderate intellectual disability and learning difficulties alongside behavioral abnormalities such as hypersociability. Several structural and functional brain alterations are characteristic of this syndrome, as well as disturbed sleep and sleeping patterns. However, the detailed physiological mechanisms underlying WBS are mostly unknown. Here, we characterized the cortical dynamics in a mouse model of WBS previously reported to replicate most of the behavioral alterations described in humans. We recorded the laminar local field potential generated in the frontal cortex during deep anesthesia and characterized the properties of the emergent slow oscillation activity. Moreover, we performed micro-electrocorticogram recordings using multielectrode arrays covering the cortical surface of one hemisphere. We found significant differences between the cortical emergent activity and functional connectivity between wild-type mice and WBS model mice. Slow oscillations displayed Up states with diminished firing rate and lower high-frequency content in the gamma range. Lower firing rates were also recorded in the awake WBS animals while performing a marble burying task and could be associated with the decreased spine density and thus synaptic connectivity in this cortical area. We also found an overall increase in functional connectivity between brain areas, reflected in lower clustering and abnormally high integration, especially in the gamma range. These results expand previous findings in humans, suggesting that the cognitive deficits characterizing WBS might be associated with reduced excitability, plus an imbalance in the capacity to functionally integrate and segregate information.This work was supported by EU H2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA2), BFU2017-85048-R Spanish Ministry of Science and CERCA Programme/Generalitat de Catalunya to MVSV

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function