Generation of new spines is often thought of as a correlate of memory and loss of spines is considered representing memory loss. Contrary to common belief, we observe that spine loss has functional value in distinctly encoding related life events rather than causing memory loss. Using spatial autocorrelation of dendritic morphology obtained from in vivo longitudinal imaging, we show that clustered loss, rather than gain, of new spines characterizes the formation of related memory. This spatially selective dendritic spine loss occurs closer to new spines formed during the acquisition of initial memory. Thus, enabling the dendrites to store multiple memories and their inter relationship. Remarkably, we find acquisition of related memory in the absence of NMDAR activation increases the fraction of such correlated spine loss.
Immediate early genes (IEGs) are widely used as a marker for neuronal plasticity. Here, we model the dynamics of IEG expression as a consecutive, irreversible first order reaction with a limiting substrate. We show that such a model, together with two-photon in vivo imaging of IEG expression, can be used to identify distinct neuronal subsets representing multiple memories. We image retrosplenial cortex (RSc) of cFOS-GFP transgenic mice to follow the dynamics of cellular changes resulting from both seizure and contextual fear conditioning behaviour. The analytical expression allowed us to segregate the neurons based on their temporal response to one specific behavioural event, thereby improving the sensitivity of detecting plasticity related neurons. This enables us to establish representation of context in RSc at the cellular scale following memory acquisition. Thus, we obtain a general method which distinguishes neurons that took part in multiple temporally separated events, by measuring fluorescence from individual neurons in live mice.SummaryIdentifying neuronal ensemble associated with different memories is vital in modern neuroscience. Meenakshi et al model and use the temporal expression dynamics of IEGs rather than thresholded intensities of the probes to identify the neurons encoding different memory in vivo.Graphical abstract
Most commonly used behavioural measures for testing learning and memory in the Morris water maze (MWM) involve comparisons of an animal s residence time in different quadrants of the pool. Such measures are limited in their ability to test different aspects of the animal s performance. Here, we describe novel measures of performance in the MWM that use vector fields to capture the motion of mice as well as their search pattern in the maze. Using these vector fields, we develop quantitative measures of performance that are intuitive and more sensitive than classical measures. First, we describe search patterns in terms of vector field properties and use these properties to define three metrics of spatial memory namely Spatial Accuracy, Uncertainty and, Intensity of Search. We demonstrate the usefulness of these measures using four different data sets including comparisons between different strains of mice, an analysis of two mouse models of Noonan syndrome (Ptpn11 D61G and Ptpn11 N308D/+), and a study of goal reversal training. Importantly, besides highlighting novel aspects of performance in this widely used spatial task, our measures were able to uncover previously undetected differences, including in an animal model of Noonan syndrome, which we rescued with the mitogen activated protein kinase kinase (MEK) inhibitor SL327. Thus, our results show that our approach breaks down performance in the Morris water maze into sensitive measurable independent components that highlight differences in spatial learning and memory in the MWM that were undetected by conventional measures.
Most commonly used behavioral measures for testing learning and memory in the Morris water maze (MWM) involve comparisons of an animal's residence time in different quadrants of the pool. Such measures are limited in their ability to test different aspects of the animal's performance. Here, we describe novel measures of performance in the MWM that use vector fields to capture the motion of mice as well as their search pattern in the maze. Using these vector fields, we develop quantitative measures of performance that are intuitive and more sensitive than classical measures. First, we describe search patterns in terms of vector field properties and use these properties to define three metrics of spatial memory namely Spatial Accuracy, Uncertainty and, Intensity of Search. We demonstrate the usefulness of these measures using four different data sets including comparisons between different strains of mice, an analysis of two mouse models of Noonan syndrome (NS; Ptpn11 D61G and Ptpn11 N308D/+), and a study of goal reversal training. Importantly, besides highlighting novel aspects of performance in this widely used spatial task, our measures were able to uncover previously undetected differences, including in an animal model of NS, which we rescued with the mitogen activated protein kinase kinase (MEK) inhibitor SL327. Thus, our results show that our approach breaks down performance in the MWM into sensitive measurable independent components that highlight differences in spatial learning and memory in the MWM that were undetected by conventional measures.
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