Distinct Roles of Rodent Thalamus and Corpus Callosum in Seizure Generalization.

OBJECTIVE: Bilateral synchronous cortical activity occurs during sleep, attention, and seizures. Canonical models place the thalamus at the center of bilateral cortical synchronization because it generates bilateral sleep spindle oscillations and primarily generalized absence seizures. However, classical studies suggest that the corpus callosum mediates bilateral cortical synchronization. METHODS: We mapped the spread of right frontal lobe-onset, focal to bilateral seizures in mice and modified it using chemo and optogenetic suppression of motor thalamic nucleus and corpus callosotomy. RESULTS: Seizures from the right cortex spread faster to the left cortex than to the left thalamus. The 2 thalami have minimal monosynaptic commissural connections compared to the massive commissure corpus callosum. Chemogenetic and closed-loop optogenetic inhibition of the right ventrolateral thalamic nucleus did not alter inter-hemispheric seizure spread. However, anterior callosotomy delayed bilateral seizure oscillations. INTERPRETATION: Thalamocortical oscillations amplify focal onset motor seizures, and corpus callosum spreads them bilaterally. ANN NEUROL 2022;91:682-696.

Neuronal circuits sustaining neocortical-injury-induced status epilepticus.

OBJECTIVES: Acute injuries or insults to the cortex, such as trauma, subarachnoid hemorrhage, lobar hemorrhage, can cause seizures or status epilepticus(SE). Neocortical SE is associated with coma, worse prognosis, delayed recovery, and the development of epilepsy. The anatomical structures progressively recruited during neocortical-onset status epilepticus (SE) is unknown. Therefore, we constructed large-scale maps of brain regions active during neocortical SE. METHODS: We used a neocortical injury-induced SE mouse model. We implanted cobalt (Co) in the right supplementary motor cortex (M2). We 16 h later administered a homocysteine injection (845 mg/kg, intraperitoneal) to C57Bl/6 J mice to induce SE and monitored it by video and EEG. We harvested animals for 1 h (early-stage) and 2 h (late-stage) following homocysteine injections. To construct activation maps, we immunolabeled whole-brain sections for cFos and NeuN, imaged them using a confocal microscope and quantified cFos immunoreactivity (IR). RESULTS: SE in the early phase consisted of discrete, focal intermittent seizures, which became continuous and bilateral in the late stage. In this early stage, cFos IR was primarily observed in the right hemisphere, ipsilateral to the Co lesion, specifically in the motor cortex, retrosplenial cortex, somatosensory cortex, anterior cingulate cortex, lateral and medial septal nuclei, and amygdala. We observed bilateral cFos IR in brain regions during the late stage, indicating the bilateral spread of focal seizures. We found increased cFOS IR in the bilateral somatosensory cortex and the motor cortex and subcortical regions, including the amygdala, thalamus, and hypothalamus. There was noticeably different, intense cFos IR in the bilateral hippocampus compared to the early stage. In addition, there was higher activity in the cortex ipsilateral to the seizure focus during the late stage compared with the early one. CONCLUSION: We present a large-scale, high-resolution map of seizure spread during neocortical injury-induced SE. Cortico-cortical and cortico subcortical re-entrant circuits sustain neocortical SE. Neuronal loss following neocortical SE, distant from the neocortical focus, may result from seizures.

Limbic progesterone receptor activity enhances neuronal excitability and seizures.

OBJECTIVE: Emerging evidence raises the possibility that progesterone receptor (PR) signaling may contribute to the reproductive hormone fluctuation-linked seizure precipitation, called catamenial epilepsy. Therefore, we studied PR isoform expression in limbic regions involved in temporal lobe epilepsy and the effect of PR activation on neuronal activity and seizures. METHODS: We evaluated PR expression in the limbic regions, entorhinal cortex (EC), hippocampus, and amygdala in female rats using quantitative real-time polymerase chain reaction (qRT-PCR). A selective agonist, Nestorone (16-methylene-17 alpha-acetoxy-19-nor-pregn-4-ene-3,20-dione) activated PRs, and the effect on excitability and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-mediated synaptic transmission of EC neurons was studied using electrophysiology. Finally, we assessed PR regulation of epileptic seizures and status epilepticus (SE) induced by lithium-pilocarpine in female rats with the global deletion of PRs (PR knockout; PRKO) using video electroencephalography (-EEG). RESULTS: Limbic regions EC, hippocampus, and amygdala robustly expressed PR messenger RNA (mRNA). Nestorone (16-methylene-17 alpha-acetoxy-19-nor-pregn-4-ene-3,20-dione) treatment reduced the action potential threshold of layer II/III EC neurons and increased the frequency of AMPA receptor-mediated synaptic currents of ovariectomized and estrogen-primed female rats. Female rats lacking PRs (PRKO) experienced a shorter duration, less intense, and less fatal SE than wild-type (WT) animals. Furthermore, Nestorone treatment caused seizure exacerbation in the WT epileptic animals, but not in the PRKO epileptic animals. SIGNIFICANCE: Activation of PRs expressed in the EC and hippocampus increased neuronal excitability and worsened seizures. These receptors may play a role in catamenial epilepsy.

Efficient Learning of Transform-Domain LMS Filter Using Graph Laplacian.

Transform-domain least mean squares (TDLMS) adaptive filters encompass the class of learning algorithms where the input data are subjected to a data-independent unitary transform followed by a power normalization stage as preprocessing steps. Because conventional transformations are not data-dependent, this preconditioning procedure was shown theoretically to improve the convergence of the least mean squares (LMS) filter only for certain classes of input data. So, one can tailor the transformation to the class of data. However, in reality, if the class of input data is not known beforehand, it is difficult to decide which transformation to use. Thus, there is a need to devise a learning framework to obtain such a preconditioning transformation using input data prior to applying on the input data. It is hypothesized that the underlying topology of the data affects the selection of the transformation. With the input modeled as a weighted finite graph, our method, called preconditioning using graph (PrecoG), adaptively learns the desired transform by recursive estimation of the graph Laplacian matrix. We show the efficacy of the transform as a generalized split preconditioner on a linear system of equations and in Hebbian-LMS learning models. In terms of the improvement of the condition number after applying the transformation, PrecoG performs significantly better than the existing state-of-the-art techniques that involve unitary and nonunitary transforms.

Construction of Local Field Potential Microelectrodes for in vivo Recordings from Multiple Brain Structures Simultaneously.

Researchers often need to record local field potentials (LFPs) simultaneously from several brain structures. Recording from multiple desired brain regions requires different microelectrode designs, but commercially available microelectrode arrays often do not offer such flexibility. Here, the present protocol outlines the straightforward design of custom-made microelectrode arrays to record LFPs from multiple brain structures simultaneously at different depths. This work describes the construction of the bilateral cortical, striatal, ventrolateral thalamic, and nigral microelectrodes as an example. The outlined design principle offers flexibility, and the microelectrodes can be modified and customized to record LFPs from any structure by calculating stereotaxic coordinates and quickly changing the construction accordingly to target different brain regions in either freely moving or anesthetized mice. The microelectrode assembly requires standard tools and supplies. These custom microelectrode arrays allow investigators to easily design microelectrode arrays in any configuration to track neuronal activity, providing LFP recordings with millisecond resolution.

Difluoroboron ß-diketonate polylactic acid oxygen nanosensors for intracellular neuronal imaging.

Critical for metabolism, oxygen plays an essential role in maintaining the structure and function of neurons. Oxygen sensing is important in common neurological disorders such as strokes, seizures, or neonatal hypoxic-ischemic injuries, which result from an imbalance between metabolic demand and oxygen supply. Phosphorescence quenching by oxygen provides a non-invasive optical method to measure oxygen levels within cells and tissues. Difluoroboron ß-diketonates are a family of luminophores with high quantum yields and tunable fluorescence and phosphorescence when embedded in certain rigid matrices such as poly (lactic acid) (PLA). Boron nanoparticles (BNPs) can be fabricated from dye-PLA materials for oxygen mapping in a variety of biological milieu. These dual-emissive nanoparticles have oxygen-insensitive fluorescence, oxygen-sensitive phosphorescence, and rigid matrix all in one, enabling real-time ratiometric oxygen sensing at micron-level spatial and millisecond-level temporal resolution. In this study, BNPs are applied in mouse brain slices to investigate oxygen distributions and neuronal activity. The optical properties and physical stability of BNPs in a biologically relevant buffer were stable. Primary neuronal cultures were labeled by BNPs and the mitochondria membrane probe MitoTracker Red FM. BNPs were taken up by neuronal cell bodies, at dendrites, and at synapses, and the localization of BNPs was consistent with that of MitoTracker Red FM. The brain slices were stained with the BNPs, and the BNPs did not significantly affect the electrophysiological properties of neurons. Oxygen maps were generated in living brain slices where oxygen is found to be mostly consumed by mitochondria near synapses. Finally, the BNPs exhibited excellent response when the conditions varied from normoxic to hypoxic and when the neuronal activity was increased by increasing K+ concentration. This work demonstrates the capability of BNPs as a non-invasive tool in oxygen sensing and could provide fundamental insight into neuronal mechanisms and excitability research.

NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity.

Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method that rises to the challenges. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell's identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features from each constituent path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, as an added benefit, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art.