Biology subjects

Skinner, F.

Publications and source records attributed to Skinner, F..

13 recordsLinked to original sources

bioRxiv2025

A Software for Identification and Characterization of Theta Rhythms in the Hippocampus

Characterizing theta rhythms in the hippocampus provides a window into understanding memory processing. An inquiry that arises when an animal sustains a pathological state is how theta rhythms are affected. In pathological states like epilepsy or Alzheimers, these rhythms change in specific ways. Statistically robust changes in these rhythms could serve as potential biomarkers, indicating the severity of the animals condition and the effectiveness of a drug. However, this understanding depends on how the data is analyzed. There are currently no standard criteria for recognizing theta dominance in experimental recordings. To address this, we have developed novel MATLAB-based software with an easy-to-use graphical user interface which enables identifying and analyzing theta rhythms in a standard way. We discuss the softwares functionality and its underlying algorithms. The algorithms were developed using previously acquired EEG/LFP data recorded from the hippocampus of a mouse kindling model of epilepsy. Two primary analyses were conducted to test the softwares functionality: first, comparing theta rhythms during the baseline period versus during spontaneous recurrent seizures; second, analyzing the timing of theta rhythms relative to the seizure event. Our illustrative results indicate that our developed software can robustly identify theta events with statistically significant feature differences. Further, the examination presented here with two mice shows that while theta events can occur just before seizures, it takes tens of minutes post-seizure before theta rhythms occur again. Our software thus provides the user with the ability to robustly identify and characterize theta rhythms and their feature changes. Significance StatementTheta rhythms in the hippocampus are fundamental for spatial navigation and memory formation. Their observed changes during several pathological states such as epilepsy and Alzheimers make them highly interesting to be able to serve as biomarkers. However, their variability (in terms of the time of occurrence, duration, and frequency range) makes them challenging to quantify. The absence of available tools for the automatic extraction of these rhythms from extended datasets significantly hampers processing efficiency and reduces accuracy and consistency. Clinicians and researchers often manually inspect their data to identify these rhythms, a process that is not only time-consuming but also inherently subjective. We have thus developed a MATLAB software for precise, automatic analysis of theta rhythms in EEG/LFP recordings.

neuroscience↗

bioRxiv2024

Ictal-Related Chirp as a Biomarker for Monitoring Seizure Progression

Despite being prevalent, the causes, mechanisms, and progression of epilepsy--a chronic neurological disorder with unprovoked seizures--are not well understood, complicating drug development for treatment. This study used a comprehensive mouse epilepsy kindling model dataset to investigate frequency modulation (chirp) as a potential indicator of distinct states of epilepsy (early evoked discharge, late evoked discharge, spontaneous recurrent seizure, and drug state). Employing time-frequency ridge extraction, chirp identification, and statistical testing, our analyses revealed that chirp patterns occur in the majority of ictal discharges (>81.6%), persisting across evoked and spontaneous seizures. While the focus was on hippocampal recordings, chirps were also detected in the piriform peripheral cortex. Significant frequency and duration changes in chirp patterns during the transition from early to late evoked ictal events suggest their potential as the screening tool for seizure progression. Additionally, detailed analyses illuminate the impact of Lorazepam, a GABAA enhancer, on chirp characteristics, providing insights into how increased inhibitory tone quantifiably influences excitatory-inhibitory balances during seizures.

neuroscience↗

bioRxiv2024

Cell-type specific contributions to theta-gamma coupled rhythms in the hippocampus

Distinct inhibitory cell types participate in cognitively relevant nested brain rhythms, and particular changes in such rhythms are known to occur in disease states. Specifically, the co-expression of theta and gamma rhythms in the hippocampus is believed to represent a general coding scheme, but cellular-based generation mechanisms for these coupled rhythms are currently unclear. We develop a population rate model of the CA1 hippocampus that encompasses circuits of three inhibitory cell types (bistratified cells, parvalbumin (PV)-expressing and cholecystokinin (CCK)-expressing basket cells) and pyramidal cells to examine this. We constrain parameters and perform numerical and theoretical analyses. The theory, in combination with the numerical explorations, predicts circuit motifs and specific cell-type mechanisms that are essential for the co-existence of theta and gamma oscillations. We find that CCK-expressing basket cells initiate the coupled rhythms and regularize theta, and PV-expressing basket cells enhance both theta and gamma rhythms. Pyramidal and bistratified cells govern the generation of theta rhythms, and PV-expressing basket and pyramidal cells play dominant roles in controlling theta frequencies. Our circuit motifs for theta-gamma coupled rhythm generation could be applicable to other brain regions. AUTHOR SUMMARYThere are many different types of inhibitory cells in our brains that are differentially affected in disease. Concomitantly, coupled rhythms change in particular ways with disease. To help understand cell-type specific changes in coupled rhythms, we develop a mathematical network model that is both respective of the cell type and also amenable to analyses. We focus on theta-gamma coupled rhythms in the hippocampus and include three different inhibitory cell types in our model circuits. By combining theoretical analysis and numerical explorations, we find distinct contributions of these inhibitory cell types to coupled rhythms, and predict motifs that are essential for the expression of theta-gamma coupled rhythms. Moving forward, we can leverage our model insights to help unravel cell-type contributions in disease states.

neuroscience↗

bioRxiv2023

Hyperpolarization-activated cation channels shape the spiking frequency preference of human cortical layer 5 pyramidal neurons

Discerning the contribution of specific ionic currents to complex neuronal dynamics is a difficult, but important, task. This challenge is exacerbated in the human setting, although the widely-characterized uniqueness of the human brain as compared to preclinical models necessitates the direct study of human neurons. Neuronal spiking frequency preference is of particular interest given its role in rhythm generation and signal transmission in cortical circuits. Here, we combine the frequency-dependent gain (FDG), a measure of spiking frequency preference, and novel in silico analyses to dissect the contributions of individual ionic currents to key FDG features of human L5 neurons. We confirm that a contemporary model of such a neuron, primarily constrained to capture subthreshold activity driven by the hyperpolarization-activated cyclic nucleotide gated (h-) current, replicates key features of the in vitro FDG both with and without h-current activity. With the model confirmed as a viable approximation of the biophysical features of interest, we applied new analysis techniques to quantify the activity of each modeled ionic current in the moments prior to spiking, revealing unique dynamics of the h-current. These findings motivated patch-clamp recordings in analogous rodent neurons to characterize their FDG, which confirmed that a biophysically-detailed model of these neurons captures key inter-species differences in the FDG. These differences are correlated with distinct contributions of the h-current to neuronal activity. Together, this interdisciplinary and multi-species study provides new insights directly relating the dynamics of the h-current to neuronal spiking frequency preference in human L5 neurons. Significance StatementUnderstanding the contributions of individual ionic currents to neuronal activity is vital, considering the established role of ion channel modifications in neuropsychiatric conditions. We combine in vitro characterization of the spiking frequency preference of human L5 cortical pyramidal neurons via the frequency-dependent gain (FDG) with new analyses of a biophysically-detailed computational model of such a neuron to delineate the connection between the dynamics of the hyperpolarization-activated cyclic nucleotide gated (h-) current prior to spiking and key properties of the FDG. By further determining that both these FDG properties and h-current dynamics are distinct in analogous rodent neurons, we provide convincing evidence for the key role of the h-current in the frequency preference of human L5 cortical neurons.

neuroscience↗

bioRxiv2022

Reduced oriens-lacunosum/moleculare (OLM)cell model identifies biophysical currentbalances for in vivo theta frequency spikingresonance

Conductance-based models have played an important role in the development of modern neuroscience. These mathematical models are powerful "tools" that enable theoretical explorations in experimentally untenable situations, and can lead to the development of novel hypotheses and predictions. With advances in cell imaging and computational power, multi-compartment models with morphological accuracy are becoming common practice. However, as more biological details are added, they make extensive explorations and analyses more challenging largely due to their huge computational expense. Here, we focus on oriens-lacunosum/moleculare (OLM) cell models. OLM cells can contribute to functionally relevant theta rhythms in the hippocampus by virtue of their ability to express spiking resonance at theta frequencies, but what characteristics underlie this is far from clear. We converted a previously developed detailed multi-compartment OLM cell model into a reduced single compartment model that retained biophysical fidelity with its underlying ion currents. We showed that the reduced OLM cell model can capture complex output that includes spiking resonance in in vivo-like scenarios as previously obtained with the multi-compartment model. Using the reduced model, we were able to greatly expand our in vivo-like scenarios. Applying spike-triggered average analyses, we were thus able to to determine that it is a combination of hyperpolarization-activated cation and muscarinic type potassium currents that specifically allow OLM cells to exhibit spiking resonance at theta frequencies. Further, we developed a robust Kalman Filtering (KF) method to estimate parameters of the reduced model in real-time. We showed that it may be possible to directly estimate conductance parameters from experiments since this KF method can reliably extract parameter values from model voltage recordings. Overall, our work showcases how the contribution of cellular biophysical current details could be determined and assessed for spiking resonance. As well, our work shows that it may be possible to directly extract these parameters from current clamp voltage recordings.

neuroscience↗

bioRxiv2022

Adaptive Unscented Kalman Filter for Neuronal State and Parameter Estimation

Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter implementations, the robust adaptive unscented Kalman filter (RAUKF) jointly estimates the states and parameters of the neuronal model while adjusting noise covariance matrices online based on innovation and residual information. We benchmark the adaptive filters performance against existing nonlinear Kalman filters and explore the sensitivity of the filter parameters to the system being modelled. To evaluate the robustness of the proposed solution, we simulate practical settings that challenge tracking performance, such as a model mismatch and measurement faults. Compared to standard variants of the Kalman filter the adaptive variant implemented here is more accurate and robust to faults.

neuroscience↗

bioRxiv2022

Cholecystokinin-expressing (CCK+) basket cells are key controllers of theta-gamma coupled rhythms in the hippocampus

Various inhibitory cell types underlie cognitively important brain rhythms and their couplings. Precisely how this is manifest is unclear. Complex network interactions make an understanding of inhibitory cell contributions extremely difficult and using experiments alone is insufficient. Using detailed biophysical models, we obtain hypotheses of how theta and gamma rhythms in the hippocampus are generated and coupled. We find critical contributions by parvalbumin-expressing (PV+) basket cells (BCs), cholecystokinin-expressing (CCK+) BCs and bistratified cells. Based on this, we develop and explore a population rate model and predict that CCK+BCs exert more control relative to PV+BCs for theta-gamma coupling, and that theta frequencies are more strongly affected by PV+BC to CCK+BC coupling relative to CCK+BC to PV+BC. As specific inhibitory cell types can be targeted during behaviour, it is possible to test these predictions. Our work shows that combining models at different scales creates new insights that otherwise would not be revealed.

neuroscience↗

bioRxiv2022

Tackling Recalcitrant Pseudomonas aeruginosa Infections In Critical Illness via Anti-virulence Monotherapy

Intestinal barrier derangement allows intestinal bacteria and their products to translocate to the systemic circulation. Pseudomonas aeruginosa (PA) superimposed infection in critically ill patients increases gut permeability and leads to gut-driven sepsis. PA infections are challenging due to multi-drug resistance (MDR), biofilms, and/or antibiotic tolerance. Inhibition of the quorum-sensing transcriptional regulator MvfR(PqsR) is a desirable anti-PA strategy as MvfR controls multiple acute and chronic virulence functions. Here we show that MvfR promotes intestinal permeability and report novel potent anti-MvfR compounds, the N-Aryl Malonamides (NAMs) resulting from extensive structure-activity-relationship studies and thorough assessment of the inhibition of MvfR-controlled virulence functions. This novel class of anti-virulence non-native ligand-based agents has a half-maximal inhibitory concentration in the nanomolar range and strong target engagement. Using a NAM lead in monotherapy protects murine intestinal barrier function, abolishes MvfR-regulated small molecules, ameliorates bacterial dissemination, and lowers inflammatory cytokines. This study demonstrates the importance of MvfR in PA-driven intestinal permeability. It underscores the utility of anti-MvfR agents in maintaining gut mucosal integrity, which should be part of any successful strategy to prevent/treat PA infections and associated gut-derived sepsis in critical illness settings. NAMs provide for the development of crucial preventive/therapeutic monotherapy options against untreatable MDR PA infections.

microbiology↗

bioRxiv2020

A hypothesis for theta rhythm frequency control in CA1 microcircuits

Computational models of neural circuits with varying levels of biophysical detail have been generated in pursuit of an underlying mechanism explaining the ubiquitous hippocampal theta rhythm. However, within the theta rhythm are at least two types with distinct frequencies associated with different behavioural states, an aspect that must be considered in pursuit of these mechanistic explanations. Here, using our previously developed excitatory-inhibitory network models that generate theta rhythms, we investigate the robustness of theta generation to intrinsic neuronal variability by building a database of heterogeneous excitatory cells and implementing them in our microcircuit model. We specifically investigate the impact of three key building block features of the excitatory cell model that underlie our model design: these cells rheobase, their capacity for post-inhibitory rebound, and their spike-frequency adaptation. We show that theta rhythms at various frequencies can arise dependent upon the combination of these building block features, and we find that the speed of these oscillations are dependent upon the excitatory cells response to inhibitory drive, as encapsulated by their phase response curves. Taken together, these findings support a hypothesis for theta frequency control that includes two aspects: (i) an internal mechanism that stems from the building block features of excitatory cell dynamics; (ii) an external mechanism that we describe as inhibition-based tuning of excitatory cell firing. We propose that these mechanisms control theta rhythm frequencies and underlie their robustness.

neuroscience↗

bioRxiv2020

Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and how their frequencies are controlled

The wide variety of cell types and their inherent biophysical complexities pose a challenge to our understanding of oscillatory activities produced by cellular-based computational models. This challenge stems from the high-dimensional and multi-parametric nature of these systems. To overcome this issue, we implement systematic comparisons of minimal and detailed models of CA1 microcircuits that generate intra-hippocampal theta rhythms (3-12 Hz). We leverage insights from minimal models to guide detailed model explorations and obtain a cellular perspective of theta generation. Our findings distinguish the pyramidal cells as the theta rhythm initiators and reveal that their activity is regularized by the inhibitory cell populations, supporting an inhibition-based tuning mechanism. We find a strong correlation between the pyramidal cell input current and the resulting LFP theta frequency, establishing that the intrinsic pyramidal cell properties underpin network frequency characteristics. This work provides a cellular-based foundation from which in vivo theta activities can be explored.

neuroscience↗

bioRxiv2019

Modeling implicates inhibitory network bistability as an underpinning of seizure initiation

A plethora of recent experimental literature implicates the abrupt, synchronous activation of GABAergic interneurons in driving the sudden change in brain activity that heralds seizure initiation. However, the mechanisms predisposing an inhibitory network toward sudden coherence specifically during ictogenesis remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyper-excitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony due to a brief perturbation. We further show that perturbations driving this transition could reasonably arise in vivo based on models of background excitatory synaptic activity in the cortex. Thus, these results propose a mechanism by which an inhibitory network can transition from incoherent to coherent dynamics in a fashion that may precipitate seizure as a downstream effect. Moreover, this mechanism specifically explains why inhibitory networks containing hyper-excitable interneurons are more vulnerable to this state change, and how such networks can undergo this transition without a permanent change in the drive to the system. This, in turn, potentially explains such networks increased vulnerability to seizure initiated by GABAergic activity.\n\nAuthor summaryFor decades, the study of epilepsy has focused on the hypothesis that over-excitation or dis-inhibition of pyramidal neurons underlies the transition from normal brain activity to seizure. However, a variety of recent experimental findings have implicated a sudden synchronous burst of activity amongst inhibitory interneurons in driving this transition. Given the counter-intuitive nature of these findings and the correspondingly novel hypothesis of seizure generation, the articulation of a feasible mechanism of action underlying this dynamic is of paramount importance for this theorys viability. Here, we use computational techniques, particularly the concept of bistability in the context of dynamical systems, to propose a mechanism for the necessary first step in such a process: the sudden synchronization of a network of inhibitory interneurons. This is the first detailed proposal of a computational mechanism explaining any aspect of this hypothesis of which we are aware. By articulating a mechanism that not only underlies this transition, but does so in a fashion explaining why ictogenic networks might be more prone to this behavior, we provide critical support for this novel hypothesis of seizure generation and potential insight into the larger question of why individuals with epilepsy are particularly vulnerable to seizure.

neuroscience↗

bioRxiv2018

Deciphering the contribution of oriens-lacunosum/moleculare (OLM) cells to intrinsic theta rhythms using biophysical local field potential (LFP) models

Oscillations in local field potentials (LFPs) commonly occur and analyses of them fuel brain function hypotheses. An understanding of the cellular correlates and pathways affecting LFPs is needed but many overlapping pathways in vivo makes this difficult to achieve. A prevalent LFP rhythm in the hippocampus is theta (3-12 Hz). Theta rhythms emerge intrinsically in an in vitro whole hippocampus preparation and thus can be produced by local interactions between interneurons and pyramidal (PYR) cells. Overlapping pathways are much reduced in this preparation making it possible to decipher the contribution of different cell types to LFP generation. We focus on oriens-lacunosum/moleculare (OLM) cells as a major class of interneurons in the hippocampus. They can influence PYR cells through two distinct pathways, (i) by direct inhibition of PYR cell distal dendrites, and (ii) by indirect disinhibition of PYR cell proximal dendrites by inhibiting bistratified cells (BiCs) that target them. We use previous inhibitory network models and build biophysical LFP models using volume conductor theory. We assess the effect of OLM cells to ongoing intrinsic LFP theta rhythms by directly comparing our model LFP features with experiment. We find that robust LFP theta responses adhering to reproducible experimental criteria occur only for particular connectivities between OLM cells and BiCs. Decomposition of the LFP reveals that OLM cell inputs onto the PYR cell regulate robustness of LFP responses without affecting average power and that the robust response depends on co-activation of distal inhibition and basal excitation. We use our models to estimate the spatial extent of the region generating LFP theta rhythms, leading us to predict that about 22,000 PYR cells participate in generating the LFP theta rhythm. Besides allowing us to understand OLM cells contributions to intrinsic theta rhythms, our work can drive hypothesis developments of cellular contributions in vivo.\n\nAuthor SummaryOscillatory local field potentials (LFPs) are extracellularly recorded potentials that are widely used to interpret information processing in the brain. For example, theta LFP rhythms (3-12 Hz) are correlated with memory processing and it is known that particular inhibitory cell types control their existence. As such, it is critical for us to appreciate how various cell types contribute to the characteristics of LFP rhythms. A precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established. However, it is difficult to assess cellular contributions in vivo because of many spatiotemporally overlapping pathways that prevent the unambiguous separation of signals. Using an in vitro preparation that exhibits intrinsic theta (3-12 Hz) rhythms and where there is much less overlap, we build biophysical LFP models to explore cell contributions to ongoing intrinsic theta rhythms. We uncover distinct contributions from different cell types and show that robust theta rhythms depend specifically on one of the cell types. We are able to determine this because our LFP models have direct links with experiment and we are able to perform thousands of simulations.

neuroscience↗

bioRxiv2017

Combining theory, model and experiment to understand how theta rhythms are generated in thehippocampus

Scientists have observed theta rhythms (3-12 Hz) in the hippocampus for decades, but we do not have a clear understanding of how they are generated. This is largely due to the complex, multi-scale and nonlinear nature of the brain. To obtain insight into mechanisms underlying the generation of theta rhythms, we develop cellular-based network models of the hippocampus based on a whole hippocampus in vitro preparation that spontaneously generates theta rhythms. Building on theoretical and computational analyses, we find that spike frequency adaptation and post-inhibitory rebound constitute a basis for theta generation in large, minimally connected CA1 pyramidal (PYR) cell network models with fast-firing parvalbumin-positive (PV+) inhibitory cells. The particular theta frequency is more controlled by PYR to PV+ cell interactions rather than PV+ to PYR cell ones. We identify two scenarios by which theta rhythms can emerge and they can be differentiated by the ratio of excitatory to inhibitory currents to PV+ cells, but not to PYR cells. Only one of the scenarios is consistent with data from the whole hippocampus preparation, which leads to the prediction that the connection probability from PV+ to PYR cells needs to be larger than from PYR to PV+ cells. Our models can serve as a platform on which to build and develop an understanding of in vivo theta generation, and of microcircuit dynamics in the hippocampus.\n\nSignificanceBrain rhythms have been linked to cognition and are disrupted in disease. This makes it essential to understand mechanisms underlying their generation. Theory and mathematical models help provide an understanding and generate hypotheses. Together with experiment they contribute a framework to dissect the cellular contributions to network activity. However, models are inherently biological approximations, and thus the specific experimental and theoretical context upon which they are built will shape their output. If the approximations and contexts are not taken into account, particularly when using previously constructed models, misinterpretations can arise. Here, we use both theory and microcircuit models derived from a specific experimental context to provide insight into cellular-based mechanisms involved in theta rhythm generation in the hippocampus.

neuroscience↗