Biology subjects

Beggs, J. M.

Publications and source records attributed to Beggs, J. M..

7 recordsLinked to original sources

bioRxiv2026

Electrocorticographic Network Feature Space Constriction as a Preictal Biomarker

In patients with epilepsy, seizures are associated with pathological neural synchronization. However, the preictal period preceding a seizure often exhibits reduced spatial synchronization compared to normal cognition. This observation aligns with the concept of the brain as a complex dynamical system, where a reduction in dimensionality and resilience can precede a phase transition. The Critical Brain Hypothesis suggests a connection between the loss of healthy scale-free behavior and various disorders, including epilepsy. Our study investigates preictal changes by utilizing network features, such as mean node degree and mean clustering coefficient, derived from thresholded correlation matrices of patient intracranial electrocorticographic electrode data. We observed a suppression of intermittent high-synchronization periods within the feature space during the minutes leading up to seizure onset. This constriction of the explored hypervolume in the preictal state indicates a breakdown in the brains ability to maintain normal coherence. We use these preictal changes to predict the probability of seizure onset using a Support Vector Machine algorithm. These discrete predictions can then be combined into real-time continuous seizure risk forecasts via Bayesian updating. This innovative and computationally lightweight approach has the potential to significantly improve upon static predictions, providing opportunities for more adaptable, quantitative, and interpretable tools for managing seizures.

neuroscience↗

bioRxiv2025

Effect of acute elevated magnesium on bursting activity and information-processing dynamics in cortical cultures

Magnesium (Mg2+) plays a significant role in hippocampal memory and learning and is implicated in a variety of neurological disorders, such as migraine. Despite this crucial role Mg2+ has on brain health, its effect on the dynamics of networks of neurons is still not fully understood. This study investigates the impact of several doses of elevated extracellular Mg2+ on cortical organotypic cultures. Cultures were recorded on a 512-microelectrode array and analyzed using the burstiness index (BI), the rate of network-wide bursts relative to other neuronal activity. We also use a suite of information theoretic measures to further establish the role Mg2+ plays in the brain. Elevated Mg2+ is found to have a dose-dependent increase on BI caused by a loss of network activity not contained within a burst or high firing rate activity. Information dynamics further show that the network experiences a loss of entropy and an increase in time-reversibility, connectivity, and transfer of redundant information. These factors indicate that Mg2+ causes a loss of complex activity and an increase in highly integrated, constant dynamics. This lays the groundwork for a deeper examination of the role Mg2+ plays in learning, memory and treating neurological disorders. AUTHOR SUMMARYMagnesium is crucial for neurological health. In pyramidal neurons, magnesium blocks NMDA receptors, thereby reducing synchronous neural activity in the brain. This has led to the canonical view of magnesium as a "burst quieter". Here, we take a closer look at magnesiums role in cortical slice cultures by examining network burstiness with normal and chronically elevated levels of magnesium. We show that magnesium can effectively increase burstiness, not reduce it. Magnesium is further shown to cause significant changes in how neurons process information. These findings may have impact how magnesium is used as a treatment for neurological disorders.

neuroscience↗

bioRxiv2023

`Mind in Vitro' platforms: Versatile, scalable, robust and open solutions to interfacing with living neurons

Motivated by the unexplored potential of in vitro neural systems for computing, and by the corresponding need of versatile, scalable interfaces for multimodal interaction, we present an accurate, modular, fully customizable and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated. A reconfigurable platform that works across multiple industry standards enables a complete signal chain, from neural substrates sampled through high-density Micro-Electrode Arrays (MEAs) to data acquisition, downstream analysis and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations and spatial topologies. Through a native analysis and management software suite, the utility and robustness of our system is demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging and long-term recording. Overall, our technology, termed Mind in Vitro to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10X cost-reduction) and open-source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.

neuroscience↗

bioRxiv2020

Synergistic neural computation is greater downstream of recurrent connectivity in organotypic cortical cultures

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration-a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow. Author SummaryNetworks compute information. That is, they modify inputs to generate distinct outputs. These computations are an important component of network information processing. Knowing how the routing of information in a network influences computation is therefore crucial. Here we asked how a key form of computation--synergistic integration--is related to the direction of local information flow in networks of spiking cortical neurons. Specifically, we asked how information flow between input neurons (i.e., recurrent information flow) and information flow from output neurons to input neurons (i.e., feedback information flow) was related to the amount of synergistic integration performed by output neurons. We found that greater synergistic integration occurred where there was more recurrent information flow. And, lesser synergistic integration occurred where there was more feedback information flow relative to feedforward information flow. These results show that computation, in the form of synergistic integration, is distinctly influenced by the directionality of local information flow. Such work is valuable for predicting where and how network computation occurs and for designing networks with desired computational abilities.

neuroscience↗

bioRxiv2020

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

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While 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 neurons 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 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 & NoteworthyDetecting 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.

neuroscience↗

bioRxiv2019

Correlated activity favors synergistic processing in local cortical networks at synaptically-relevant timescales

Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05-14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission--of which synergistic processing is a component--that resulted as mutual information grew. However, at extrasynaptic windows (up to 3000 ms), where mutual information values were high, the relationship between mutual information and synergistic processing became negative. In this regime, greater mutual information resulted in a disproportionate increase in redundancy relative to information transmission. These results indicate that the emergence of synergistic processing from correlated activity differs according to timescale and correlation regime. In a low-correlation regime, synergistic processing increases with greater correlation, and in a high correlation regime, synergistic processing decreases with greater correlation. AUTHOR SUMMARYIn the present work, we address the question of whether correlated activity in functional networks of cortical circuits supports neural computation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that, at timescales most relevant to direct neuronal communication, neurons with more correlated activity predicted greater computation, suggesting that correlated activity does support computation in cortical circuits. Importantly, this result reversed at timescales less relevant to direct neuronal communication, where even greater correlated activity predicted decreased computation. Thus, the relationship between correlated activity and computation depends on the timescale and the degree of correlation in neuronal interactions.

neuroscience↗

bioRxiv2018

Computation is concentrated in rich clubs of local cortical neurons

To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit topology. Rich-clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich-clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average [~]300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute [~]200% more information than neurons outside of the rich club. Indeed, the amount of computation performed in the rich-club was proportional to the amount information propagation by the same neurons. This suggests that, in these circuits, information propagation drives computation. Comparing the computation-to-propagation ratio inside versus outside of the rich club showed that rich clubs compute at a slightly, though significantly, reduced level ([~]4% lower). In total, our findings indicate that rich club topology in effective cortical circuits supports not only information propagation but also neural computation.\n\nAUTHOR SUMMARYHere we answer the question of whether rich club topology in functional cortical circuits supports neural computation as it has been previously shown to do for information propagation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that neurons in rich clubs computed significantly more than neurons outside of rich clubs, suggesting that rich-clubs do support computation in cortical circuits. Indeed, the amount of computation that we found in the rich club was proportional to the amount of information they propagate suggesting that, in these circuits, information propagation drives computation.

neuroscience↗