Biology subjects

Gonzalez, O. C.

Publications and source records attributed to Gonzalez, O. C..

10 recordsLinked to original sources

bioRxiv2026

Cell-type targeted CRISPR/Cas9 Clock knockdown in mouse VTA dopamine neurons alter sleep, behavior, and cellular excitability

Bipolar disorder (BD) is a severe psychiatric disease characterized by recurrent mania, depression, and circadian rhythm disruption. Among circadian regulators implicated in mood-related dysfunction, Clock has emerged as a particularly strong mechanistic candidate. However, cell type-specific functions of Clock within mood-relevant circuits remain incompletely defined. Here, we developed and applied a Cre-dependent AAV-SaCas9 gene-editing strategy to disrupt Clock selectively in ventral tegmental area dopamine neurons. We first established a rapid in vitro screening pipeline for guide RNA selection that accurately predicted in vivo editing efficiency. We then targeted Clock in vivo using a single AAV-based editing strategy and observed robust titer-dependent reduction of Clock expression, by targeted sequencing, in situ hybridization, and immunohistochemistry. We assessed the functional consequences of Clock disruption across analysis levels, including a behavioral battery, circadian and sleep-wake measurements using EEG and EMG, and electrophysiological recordings. These results establish a practical framework for rapid, cell-type-specific disruption of candidate psychiatric risk genes and provide a mechanistically grounded model for investigating how loss of Clock function in mesolimbic dopaminergic circuits contributes to BD-relevant phenotypes.

neuroscience↗

bioRxiv2025

Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning

Sleep replay - the reactivation of memory traces during slow-wave sleep - is widely held to stabilize memories and reduce interference, yet exactly how replay reorganizes synaptic-weight space to preserve existing memories while incorporating new ones remains unclear. Here, we use a biophysically realistic network model to probe the synaptic-weight dynamics underlying this process. We find that replay drives synaptic weights toward stable configurations - synaptic attractors - that jointly support both old and new memories. Hippocampus-driven interactions between sharp-wave ripples and cortical slow waves guide this reorganization, allowing recently acquired memories to be incorporated without degrading prior ones. These results reveal a mechanistic and geometric framework for memory consolidation: sleep does not passively protect memories, but actively sculpts synaptic-weight space into attractor configurations from which forgetting requires escaping a stability basin. SIGNIFICANCE STATEMENTStoring, processing, and retrieving information underpins intelligent behavior. Sleep extracts invariant features from prior experience, promoting the emergence of explicit knowledge and insight. Yet despite abundant empirical findings, our understanding of how sleep reshapes memory representations at the level of synaptic organization remains limited. Here we present a novel framework that describes how memories are encoded in synaptic-weight space and how sleep dynamics reorganize synaptic landscape. These results advance our understanding of how the brain solves core problems of lifelong learning.

neuroscience↗

bioRxiv2025

Intrinsic ion dynamics underlies the temporal nature of resting-state functional connectivity

The neural mechanisms underlying the emergence of functional connectivity in resting-state fMRI remain poorly understood. Recent studies suggest that resting-state activity consists of brief periods of strong co-fluctuations among brain regions, which reflect overall functional connectivity. Others report a continuum in co-fluctuations over time, with varying degree of correlation to functional connectivity. These findings raise the critical question: what neural processes underlie the temporal structure of resting-state activity? To address this, we used a biophysically realistic whole-brain computational model in which resting-state activity emerged from temporal variations in the ion concentrations of potassium (K+) and sodium (Na+), intracellular chloride (Cl-), and the activity of the Na+/K+ ATPase. The model reproduced transient periods of high co-fluctuations, and the functional connectivity at different co-fluctuation levels correlated to varying degrees with the connectivity measured over the entire simulation, in line with experimental observations. The periods of high co-fluctuations were aligned with large changes in extracellular ion concentrations. Furthermore, critical parameters governing ion dynamics strongly affected both the timing of these transient events and the spatial structure of the resulting functional connectivity. The balance of excitatory and inhibitory activity further modulated their frequency and amplitude. Together, these results suggest that intrinsic fluctuations in ion dynamics could serve as a plausible neural mechanism for the temporal organization of co-fluctuations and resting-state functional connectivity.

neuroscience↗

bioRxiv2025

Interleaved Replay of Novel and Familiar Memory Traces During Slow-Wave Sleep Prevents Catastrophic Forgetting

Humans and animals can learn continuously, acquiring new knowledge while integrating it into a lifelong memory pool. In contrast, artificial neural networks (ANNs) suffer from catastrophic forgetting, where new training disrupts existing memories. This issue can be alleviated in ANNs by interleaving training on new tasks with past data; however, whether the brain uses a similar strategy is unknown. In this work, we show that slow-wave sleep interleaves replay of familiar and novel (i.e. hippocampal-dependent) memory traces within individual slow waves, allowing new memories to integrate into the existing cortical pool without interference. This study presents a novel theory for how memory traces acquired across an animals life are organized within the cortical-hippocampal system to support continual learning and suggests novel principles for a broad range of continual learning AI.

neuroscience↗

bioRxiv2024

Aging and injury drive neuronal senescence in the dorsal root ganglia

Aging negatively impacts central nervous system function; however, the cellular impact of aging in the peripheral nervous system remains poorly understood. Aged individuals are more likely to experience increased pain and slower recovery after trauma. Such injury can damage vulnerable peripheral axons of dorsal root ganglion (DRG) neurons resulting in somatosensory dysfunction. One cellular mechanism common to both aging and injury is cellular senescence, a complex cell state that can contribute to the aged pro-inflammatory environment. We uncovered, for the first time, DRG neuron senescence in the context of aging and pain-inducing peripheral nerve injury in young and aged mice. Aged DRG neurons displayed multiple markers of senescence (SA-{beta}-gal, p21, p16, IL6) when compared to young DRG neurons. Peripheral nerve injury triggered a further accumulation of senescent DRG neurons over time post-injury in young and aged DRG. These senescent neurons were dynamic and heterogeneous in their expression of senescence markers, p16, p21, and senescence-associated secretory phenotype (SASP) expression of IL6, which was influenced by age. An electrophysiological characterization of senescence marker-expressing neurons revealed high-firing and nociceptor-like phenotypes within these populations. In addition, we observed improvement in nociceptive behaviors in young and aged nerve-injured mice after treatment with a senolytic agent that eliminates senescent cells. Finally, we confirmed in human post-mortem DRG samples that neuronal senescence is present and increases with age. Overall, we describe a susceptibility of the peripheral nervous system to neuronal senescence with age or injury that may be a targetable mechanism to treat sensory dysfunction, such as chronic pain, particularly in aged populations.

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bioRxiv2023

Emergent effects of synaptic connectivity on cortical sleep slow wave amplitude, density and propagation in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease. Author SummarySlow Wave Sleep (SWS) is a primary brain state displayed during Non-Rapid Eye Movement (NREM) sleep. While previously thought of as homogenous waves of activity that sweep across the entire brain, modern research has suggested a more nuanced pattern of activity that can vary between local and global slow wave activity. However, understanding how these states emerge from small scale neuronal dynamics and network connectivity remains unclear. We developed a biophysically realistic model of the human brain capable of generating SWS-like behavior, and investigated the role of connectivity in the spatio-temporal dynamics of these slow waves. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network behavior - specifically, models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. These results were compared to human data, and we found that models producing mixed states provided the best match to the network behavior and functional connectivity of human subject data. These findings shed light on how the spatio-temporal properties of SWS emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

neuroscience↗

bioRxiv2019

Can sleep protect memories from catastrophic forgetting?

Continual learning remains to be an unsolved problem in artificial neural networks. Biological systems have evolved mechanisms by which they can prevent catastrophic forgetting of old knowledge during new training and allow lifelong learning. Building upon data suggesting the importance of sleep in learning and memory, here we test a hypothesis that sleep protects memories from catastrophic forgetting. We found that training in a thalamocortical network model of a \"new\" memory that interferes with previously stored \"old\" memory may result in degradation and forgetting of the old memory trace. Simulating NREM sleep immediately after new learning leads to replay, which reverses the damage and ultimately enhances both old and new memory traces. Surprisingly, we found that sleep replay goes beyond recovering old memory traces that were damaged by new learning. When a new memory competes for the neuronal/synaptic resources previously allocated to the old memory, sleep replay changes the synaptic footprint of the old memory trace to allow for the overlapping populations of neurons to store multiple memories. Different neurons become preferentially supporting different memory traces to allow successful recall. We compared synaptic weight dynamics during sleep replay with that during interleaved training - a common approach to overcome catastrophic forgetting in artificial networks - and found that interleaved training promotes synaptic competition and weakening of reciprocal synapses, effectively reducing an ensemble of neurons contributing to memory recall. This leads to suboptimal recall performance compared to that after sleep. Together, our results suggest that sleep provides a powerful mechanism to achieve continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize memory interference.

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bioRxiv2018

Characterizing concentration-dependent neural dynamics of 4-aminopyridine-induced epileptiform activity

Epilepsy remains one of the most common neurological disorders. In patients, it is characterized by unprovoked, spontaneous, and recurring seizures or ictal events. Typically, inter-ictal events or large bouts of population level activity can be measured between seizures and are generally asymptomatic. Decades of research has focused on understanding the mechanisms leading to the development of seizure-like activity using various proconvulsive pharmacological agents, including 4-aimnopyridine (4AP). However, the lack of consistency in the concentrations used for studying 4AP-induced epileptiform activity in animal models may give rise to differences in the results and interpretation thereof. Indeed, the range of 4AP concentration in both in vivo and in vitro studies varies from 3M to 40mM. Here, we explored the effects of various 4AP concentrations on the development and characteristics of hippocampal epileptiform activity in acute mouse brain slices of either sex. Using multielectrode array recordings, we show that 4AP induces hippocampal epileptiform activity for broad range of concentrations. The frequency component and the spatio-temporal patterns of the epileptiform activity revealed a dose-dependent response. Finally, in the presence of 4AP, reduction of KCC2 co-transporter activity by KCC2 antagonist VU0240551 prevented the manifestation of the frequency component differences between different concentrations of 4AP. Overall, the study predicts that different concentrations of 4AP can result in the different mechanisms behind hippocampal epileptiform activity, of which some are dependent on the KCC2 co-transporter function.

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bioRxiv2017

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Resting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

neuroscience↗

bioRxiv2017

Role of KCC2-Dependent Potassium Efflux in 4-Aminopyridine-Induced Epileptiform Synchronization

A balance between excitation and inhibition is required to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be untrue, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the co-transporter KCC2 in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices of either sex revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). Further, we developed a biophysically realistic network model implementing complex dynamics of the ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Model predicted that interneuron stimulation triggered interneuron firing that was accompanied by an increase in intracellular chloride and a subsequent KCC2-dependent gradual accumulation of extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.

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