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1. Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences

   https://doi.org/10.1038/s41598-020-59227-5  

   Renteria, Carlos; Liu, Yuan-Zhi; Chaney, Eric J.; Barkalifa, Ronit; Sengupta, Parijat; Boppart, Stephen A. (February 2020, Scientific Reports)

   Abstract Propagation of signals between neurons and brain regions provides information about the functional properties of neural networks, and thus information transfer. Advances in optical imaging and statistical analyses of acquired optical signals have yielded various metrics for inferring neural connectivity, and hence for mapping signal intercorrelation. However, a single coefficient is traditionally derived to classify the connection strength between two cells, ignoring the fact that neural systems are inherently time-variant systems. To overcome these limitations, we utilized a time-varying Pearson’s correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12–15 hippocampal neurons from GCaMP6s mice after applying various concentrations of glutamate. Results provide a comprehensive overview of resulting firing patterns, network connectivity, signal directionality, and network properties. Together, these metrics provide a more comprehensive and robust method of analyzing transient neural signals, and enable future investigations for tracking the effects of different stimuli on network properties. 

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2. Time-dependent homeostatic mechanisms underlie brain-derived neurotrophic factor action on neural circuitry

   https://doi.org/10.1038/s42003-023-05638-9  

   O’Neill, Kate_M; Anderson, Erin_D; Mukherjee, Shoutik; Gandu, Srinivasa; McEwan, Sara_A; Omelchenko, Anton; Rodriguez, Ana_R; Losert, Wolfgang; Meaney, David_F; Babadi, Behtash; et al (December 2023, Communications Biology)

   Abstract Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. From our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider. 

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3. Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

   https://doi.org/10.1152/jn.00066.2020  

   Ren, Naixin; Ito, Shinya; Hafizi, Hadi; Beggs, John M.; Stevenson, Ian H. (December 2020, Journal of Neurophysiology)

   null (Ed.)

   Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. Although previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the generalized linear model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron’s type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from the mouse somatosensory cortex. Here, our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research. NEW & NOTEWORTHY Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data. 

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4. Synapses without tension fail to fire in an in vitro network of hippocampal neurons

   https://doi.org/10.1073/pnas.2311995120  

   Joy, Md Saddam; Nall, Duncan L.; Emon, Bashar; Lee, Ki Yun; Barishman, Alexandra; Ahmed, Movviz; Rahman, Saeedur; Selvin, Paul R.; Saif, M. Taher (December 2023, Proceedings of the National Academy of Sciences)

   Neurons in the brain communicate with each other at their synapses. It has long been understood that this communication occurs through biochemical processes. Here, we reveal that mechanical tension in neurons is essential for communication. Using in vitro rat hippocampal neurons, we find that 1) neurons become tout/tensed after forming synapses resulting in a contractile neural network, and 2) without this contractility, neurons fail to fire. To measure time evolution of network contractility in 3D (not 2D) extracellular matrix, we developed an ultrasensitive force sensor with 1 nN resolution. We employed Multi-Electrode Array and iGluSnFR, a glutamate sensor, to quantify neuronal firing at the network and at the single synapse scale, respectively. When neuron contractility is relaxed, both techniques show significantly reduced firing. Firing resumes when contractility is restored. This finding highlights the essential contribution of neural contractility in fundamental brain functions and has implications for our understanding of neural physiology. 

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5. Induction of Temporal Lobe Epilepsy in Mice with Pilocarpine

   https://doi.org/10.21769/BioProtoc.3533  

   Arshad, M; Naegele, J. (January 2020, Bioprotocol)

   null (Ed.)

   In the pilocarpine model of temporal lobe epilepsy (TLE) in rodents, systemic injections of pilocarpine induce continuous, prolonged limbic seizures, a condition termed “Status Epilepticus” (SE). With appropriate doses, many inbred strains of mice show behavioral seizures within an hour after pilocarpine is injected. With the behavioral scoring system based on a modification of the original Racine scale, one can monitor the seizures behaviorally, as they develop into more prolonged seizures and SE. SE is typically associated with damage to subsets of hippocampal neurons and other structural changes in the hippocampus and generally subsides on its own. However, more precise control of the duration of SE is commonly achieved by injecting a benzodiazepine into the mouse 1 to 3 h after the onset of SE to suppress the seizures. Several days following pilocarpine-induced SE, electrographic and behavioral seizures begin to occur spontaneously. The goal of this protocol is to reliably generate mice that develop spontaneous recurrent seizures (SRS) and show the typical neuropathological changes in the brain characteristic of severe human mesial temporal lobe epilepsy (mTLE), without high mortality. To reduce mortality, multiple subthreshold injections of pilocarpine are administered, which increases the percentage of mice developing SE without concomitant mortality. Precise control of the duration of SE (1 or 3 h) is achieved by suppressing SE with the benzodiazepine Midazolam (Versed). We have found that this protocol is an efficient means for generating mice that subsequently develop characteristics of human mTLE including high-frequency interictal spike and wave activity and SRS. In addition, we and others have shown that this protocol produces mice that show excitotoxic cell death of subsets of hippocampal GABAergic interneurons, particularly in the dentate gyrus and compensatory sprouting of excitatory projections from dentate granule cells (mossy fiber sprouting). Aspects of this protocol have been described in several of our previous publications. 

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