kCSD-python, reliable current source density estimation with quality control.

Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.

Brain size, gut size and cognitive abilities: the energy trade-offs tested in artificial selection experiment.

The enlarged brains of homeotherms bring behavioural advantages, but also incur high energy expenditures. The 'expensive brain' (EB) hypothesis posits that the energetic costs of the enlarged brain and the resulting increased cognitive abilities (CA) were met by either increased energy turnover or reduced allocation to other expensive organs, such as the gut. We tested the EB hypothesis by analysing correlated responses to selection in an experimental evolution model system, which comprises line types of laboratory mice selected for high or low basal metabolic rate (BMR), maximum (VO2max) metabolic rates and random-bred (unselected) lines. The traits are implicated in the evolution of homeothermy, having been pre-requisites for the encephalization and exceptional CA of mammals, including humans. High-BMR mice had bigger guts, but not brains, than mice of other line types. Yet, they were superior in the cognitive tasks carried out in both reward and avoidance learning contexts and had higher neuronal plasticity (indexed as the long-term potentiation) than their counterparts. Our data indicate that the evolutionary increase of CA in mammals was initially associated with increased BMR and brain plasticity. It was also fuelled by an enlarged gut, which was not traded off for brain size.

What we can and what we cannot see with extracellular multielectrodes.

Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. We show that with conceptual separation of the estimation space from experimental setup one can recover sources not accessible to standard methods.

PSD-95 in CA1 Area Regulates Spatial Choice Depending on Age.

Cognitive processes that require spatial information rely on synaptic plasticity in the dorsal CA1 area (dCA1) of the hippocampus. Since the function of the hippocampus is impaired in aged individuals, it remains unknown how aged animals make spatial choices. Here, we used IntelliCage to study behavioral processes that support spatial choices of aged female mice living in a group. As a proxy of training-induced synaptic plasticity, we analyzed the morphology of dendritic spines and the expression of a synaptic scaffold protein, PSD-95. We observed that spatial choice training in young adult mice induced correlated shrinkage of dendritic spines and downregulation of PSD-95 in dCA1. Moreover, long-term depletion of PSD-95 by shRNA in dCA1 limited correct choices to a reward corner, while reward preference was intact. In contrast, old mice used behavioral strategies characterized by an increased tendency for perseverative visits and social interactions. This strategy resulted in a robust preference for the reward corner during the spatial choice task. Moreover, training decreased the correlation between PSD-95 expression and the size of dendritic spines. Furthermore, PSD-95 depletion did not impair place choice or reward preference in old mice. Thus, our data indicate that while young mice require PSD-95-dependent synaptic plasticity in dCA1 to make correct spatial choices, old animals observe cage mates and stick to a preferred corner to seek the reward. This strategy is resistant to the depletion of PSD-95 in the CA1 area. Overall, our study demonstrates that aged mice combine alternative behavioral and molecular strategies to approach and consume rewards in a complex environment.SIGNIFICANCE STATEMENT It remains poorly understood how aging affects behavioral and molecular processes that support cognitive functions. It is, however, essential to understand these processes to develop therapeutic interventions that support successful cognitive aging. Our data indicate that while young mice require PSD-95-dependent synaptic plasticity in dCA1 to make correct spatial choices (i.e., choices that require spatial information), old animals observe cage mates and stick to a preferred corner to seek the reward. This strategy is resistant to the depletion of PSD-95 in the CA1 area. Overall, our study demonstrates that aged mice combine alternative behavioral and molecular strategies to approach and consume rewards in a complex environment. Second, the contribution of PSD-95-dependent synaptic functions in spatial choice changes with age.

Chronic fluoxetine treatment impairs motivation and reward learning by affecting neuronal plasticity in the central amygdala.

BACKGROUND AND PURPOSE: The therapeutic effects of fluoxetine are believed to be due to increasing neuronal plasticity and reversing some learning deficits. Nevertheless, a growing amount of evidence shows adverse effects of this drug on cognition and some forms of neuronal plasticity. EXPERIMENTAL APPROACH: To study the effects of chronic fluoxetine treatment, we combine an automated assessment of motivation and learning in mice with an investigation of neuronal plasticity in the central amygdala and basolateral amygdala. We use immunohistochemistry to visualize neuronal types and perineuronal nets, along with DI staining to assess dendritic spine morphology. Gel zymography is used to test fluoxetine's impact on matrix metalloproteinase-9, an enzyme involved in synaptic plasticity. KEY RESULTS: We show that chronic fluoxetine treatment in non-stressed mice increases perineuronal nets-dependent plasticity in the basolateral amygdala, while impairing MMP-9-dependent plasticity in the central amygdala. Further, we illustrate how the latter contributes to anhedonia and deficits of reward learning. Behavioural impairments are accompanied by alterations in morphology of dendritic spines in the central amygdala towards an immature state, most likely reflecting animals' inability to adapt. We strengthen the link between the adverse effects of fluoxetine and its influence on MMP-9 by showing that behaviour of MMP-9 knockout animals remains unaffected by the drug. CONCLUSION AND IMPLICATIONS: Chronic fluoxetine treatment differentially affects various forms of neuronal plasticity, possibly explaining its opposing effects on brain and behaviour. These findings are of immediate clinical relevance since reported side effects of fluoxetine pose a potential threat to patients.

PyMICE: APython library for analysis of IntelliCage data.

IntelliCage is an automated system for recording the behavior of a group of mice housed together. It produces rich, detailed behavioral data calling for new methods and software for their analysis. Here we present PyMICE, a free and open-source library for analysis of IntelliCage data in the Python programming language. We describe the design and demonstrate the use of the library through a series of examples. PyMICE provides easy and intuitive access to IntelliCage data, and thus facilitates the possibility of using numerous other Python scientific libraries to form a complete data analysis workflow.