Biology subjects

Jones, M. V.

Publications and source records attributed to Jones, M. V..

6 recordsLinked to original sources

bioRxiv2018

Temporal pattern separation in hippocampal neurons through multiplexed neural codes

Pattern separation is a central concept in current theories of episodic memory: this computation is thought to support our ability to avoid confusion between similar memories by transforming similar cortical input patterns of neural activity into dissimilar output patterns before their long-term storage in the hippocampus. Because there are many ways one can define patterns of neuronal activity and the similarity between them, pattern separation could in theory be achieved through multiple coding strategies. Using our recently developed assay that evaluates pattern separation in isolated tissue by controlling and recording the input and output spike trains of single hippocampal neurons, we explored neural codes through which pattern separation is performed by systematic testing of different similarity metrics and various time resolutions. We discovered that granule cells, the projection neurons of the dentate gyrus, can exhibit both pattern separation and its opposite computation, pattern convergence, depending on the neural code considered and the statistical structure of the input patterns. Pattern separation is favored when inputs are highly similar, and is achieved through spike time reorganization at short time scales (< 100 ms) as well as through variations in firing rate and burstiness at longer time scales. These multiplexed forms of pattern separation are network phenomena, notably controlled by GABAergic inhibition, that involve many celltypes with input-output transformations that participate in pattern separation to different extent and with complementary neural codes: a rate code for dentate fast-spiking interneurons, a burstiness code for hilar mossy cells and a synchrony code at long time scales for CA3 pyramidal cells. Therefore, the isolated hippocampal circuit itself is capable of performing temporal pattern separation using multiplexed coding strategies that might be essential to optimally disambiguate multimodal mnemonic representations.\n\nAuthor SummaryPattern separation (the process of disambiguating incoming patterns of neuronal activity) is a central concept in all current theories of episodic memory, as it is hypothesized to support our ability to avoid confusion between similar memories. For the last thirty years, pattern separation has been attributed to the dentate gyrus of the hippocampus, but this has been hard to test experimentally. Moreover, because it is unclear how to define activity patterns in the brain, such a computation could be achieved in many different ways. Here, we demonstrate that pattern separation is performed by hippocampal networks (dentate gyrus and CA3) through a variety of neural codes. By systematically testing different definitions of what it means for spike trains to be similar (using a range of time scales and various standard and innovative metrics that assume different views of the neural code), we assessed how the input-output transformation of multiple hippocampal celltypes relate to pattern separation and found that different celltypes favor complementary coding strategies. This might help storing rich but concise and unambiguous representations of complex events. Finally, we provide the first experimental evidence of the importance of inhibitory signals in mediating pattern separation, and identify through which coding strategies.

neuroscience↗

bioRxiv2018

A Predictive Epilepsy Index Based on Probabilistic Classification of Interictal Spike Waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate-versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

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bioRxiv2018

On the problem of spike-wave discharge detection by humans and machines.

Summary and KeywordsO_ST_ABSObjectiveC_ST_ABSManual detection of spike-wave discharges (SWDs) from EEG records is time intensive, costly, and subject to inconsistencies/biases. Additionally, manual scoring often omits information on SWD confidence/intensity which may be important for the investigation of mechanistic-based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of pre-selected events to establish a confidence-based, continuous-valued scoring.\n\nMethodsWe develop a support vector machine (SVM)-based algorithm for the automated detection of SWDs in the {gamma}2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency- and amplitude-based peak detection. Four humans scored a set of 2500 putative events identified by the algorithm. Then, using predictors calculated from the wavelet transform of each event and the labels from human scoring, we trained a SVM to classify (SWD/nonSWD) and assign confidence scores to each event identified from 60 24-hour EEG records. We provide a detailed assessment of intra- and inter-rater scoring that demonstrates advantages of automated scoring.\n\nResultsThe algorithm scored SWDs along a continuum that is highly correlated with human confidence and that allows us to more effectively characterize ambiguous events. We demonstrate that events along our scoring continuum are temporally and proportionately correlated with abrupt changes in spectral power bands relevant to normal behavioral states including sleep.\n\nSignificanceWhile there are automated and semi-automated methods for the detection of SWDs in humans and rats, we contribute to the need for continued development of SWD detection in mice. Our results demonstrate the value of viewing detection of SWDs as a continuous classification problem to better understand ground truth in SWD detection (i.e., the most reliable features agreed upon by humans that also correlate with objective physiological measures).\n\nKey Point BoxO_LIClinicians and researchers may benefit from an automated method of SWD detection that provides a framework for the quantitative description of SWDs and how they relate to other electrographic events.\nC_LIO_LIWe present an algorithm for the automated, consistent, and rapid scoring of SWDs that assigns a confidence to detected events that is highly correlated with human scoring confidence.\nC_LIO_LIWe characterize the human inter- and intra-rater consistency in the scoring of potential SWD events and compare them with the algorithm.\nC_LIO_LIEvents along the scoring continuum generated by the algorithm are temporally and proportionately correlated with changes in spectral power bands relevant to behavioral states including sleep.\nC_LI

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bioRxiv2017

Cortical Tonic Inhibition Regulates the Expression of Spike-and-Wave Discharges Associated with Absence Epilepsy.

Synchronous and bilateral spike-and-wave discharges accompany nonconvulsive behavioral and cognitive arrest during seizures associated with absence epilepsy. Previous investigation of multiple absence animal models suggests that the underlying cause of absence seizures is an increase in thalamic inhibitory tonic currents. In contrast, in this study we provide evidence that the level of cortical tonic inhibition also regulates absence seizure expression. Using continuous video-EEG recordings to monitor absence seizures and spike-and-wave discharge expression we show that pharmacological blockade of cortical tonic inhibition provokes absence seizures in wild-type mice. Furthermore, we show that pharmacological rescue of cortical tonic inhibition in an absence mouse ({gamma}2R43Q) model, which lacks tonic inhibition, suppresses absence seizure and spike-and-wave discharge expression. Collectively, these results suggest an optimum level of tonic inhibition in the thalamocortical circuit is required for normal functioning and that a deviation from this optimum results in aberrant thalamocortical function, SWDs and absence seizures.

neuroscience↗

bioRxiv2017

Tonic Inhibition is Abolished in GABAA Receptor γ2R43Q Knock-in Mice with Absence Epilepsy and Febrile Seizures.

The {gamma}2R43Q GABAA receptor mutation confers absence epilepsy in humans, and {gamma}2R43Q knock-in mice (RQ) display absence seizures and generalized spike-and-wave discharges reminiscent of their human counterparts. Previous work on several rodent models led to the conclusion that elevated tonic inhibition in thalamic neurons is necessary and sufficient to produce typical absence epilepsy. In contrast, here we used patch-clamp electrophysiology in brain slices to show that RQ mice entirely lack tonic inhibition in principal cells of layer II/III somatosensory cortex and ventrobasal thalamus. Additionally, protein quantification and multielectrode electrophysiology show that the mutation interferes with trafficking of GABAA receptor subunits involved in generating tonic currents, leading to increased cortical firing and decreased thalamic bursting rates. Together with previous work, our results suggest that an optimum level of tonic inhibition is required for normal thalamocortical function, such that deviations in either direction away from this optimum enhance susceptibility to absence seizures.

neuroscience↗

bioRxiv2017

Pattern separation of spiketrains by individual granule cells of the dentate gyrus

Pattern separation is a process that minimizes overlap between patterns of neuronal activity representing similar experiences. Theoretical work suggests that the dentate gyrus (DG) performs this role for memory processing but a direct demonstration is lacking. One limitation is the difficulty to measure DG inputs and outputs simultaneously. To rigorously assess pattern separation by DG circuitry, we used mouse brain slices to stimulate DG afferents and simultaneously record DG granule cells (GCs) and interneurons. Output spiketrains of GCs are more dissimilar than their input spiketrains, demonstrating for the first time temporal pattern separation at the level of single neurons in the DG. Pattern separation is larger in GCs than in fast-spiking interneurons and hilar mossy cells, and is amplified in CA3 pyramidal cells. Analysis of the neural noise and computational modelling suggest that this form of pattern separation is not explained by simple randomness and arises from specific presynaptic dynamics. Overall, by reframing the concept of pattern separation in dynamic terms and by connecting it to the physiology of different types of neurons, our study offers a new window of understanding in how hippocampal networks might support episodic memory.

neuroscience↗