Biology subjects

Khanzada, S.

Publications and source records attributed to Khanzada, S..

6 recordsLinked to original sources

bioRxiv2026

Experience Reorganizes Coordinated Population Dynamics Across Hippocampal Circuits

Hippocampal function relies on structured patterns of population activity that reflect the intrinsic organization of the circuit. Although experience is known to influence synaptic properties and local activity features, whether prolonged experience reshapes the large-scale organization of population dynamics across hippocampal subfields in the intact brain has remained unclear. Progress on this question has been limited by the lack of measurements that capture circuit-wide activity simultaneously while preserving spatial circuit structure. Here, we combined prolonged environmental experience with large-scale, simultaneous multi-shank recordings spanning CA1, CA3, and dentate gyrus in the mouse hippocampus to examine experience-dependent changes in circuit dynamics. We find that long-term experience reorganizes the basal operating state of the hippocampus, altering the statistical structure of field potentials, population spiking, and sharp-wave ripple activity in a coordinated manner across subfields. These changes are not confined to specific regions but instead reflect a circuit-wide reconfiguration of functional interactions that extends over larger spatial scales. At the population level, experience shifts hippocampal dynamics into a more coordinated dynamical regime, characterized by shared low-dimensional structure across hippocampal subfields. Together, these findings demonstrate that long-term experience reorganizes coordinated population dynamics across hippocampal circuits, establishing an intrinsic activity organization that reflects the experiential history of a memory network.

neuroscience↗

bioRxiv2024

DENOISING: Dynamic Enhancement and Noise Overcoming in Multimodal Neural Observations via High-density CMOS-based Biosensors

Large-scale multimodal neural recordings on high-density biosensing microelectrode arrays (HD-MEAs) offer unprecedented insights into the dynamic interactions and connectivity across various brain networks. However, the fidelity of these recordings is frequently compromised by pervasive noise, which obscures meaningful neural information and complicates data analysis. To address this challenge, we introduce DENOISING, a versatile data-derived computational engine engineered to adjust thresholds adaptively based on large-scale extracellular signal characteristics and noise levels. This facilitates the separation of signal and noise components without reliance on specific data transformations. Uniquely capable of handling a diverse array of noise types (electrical, mechanical, and environmental) and multidimensional neural signals, including stationary and non-stationary oscillatory local field potential (LFP) and spiking activity, DENOISING presents an adaptable solution applicable across different recording modalities and brain networks. Applying DENOISING to large-scale neural recordings from mice hippocampal and olfactory bulb networks yielded enhanced signal-to-noise ratio (SNR) of LFP and spike firing patterns compared to those computed from raw data. Comparative analysis with existing state-of-the-art denoising methods, employing SNR and root mean square noise (RMS), underscores DENOISINGs performance in improving data quality and reliability. Through experimental and computational approaches, we validate that DENOISING improves signal clarity and data interpretation by effectively mitigating independent noise in spatiotemporally structured multimodal datasets, thus unlocking new dimensions in understanding neural connectivity and functional dynamics.

neuroscience↗

bioRxiv2024

Mapping Large-scale Spatiotemporal Dynamics of Synaptic Plasticity and LTP for Memory Encoding in the Hippocampal Network

Understanding memory formation requires elucidating the intricate dynamics of neuronal networks in the hippocampus, where information is encoded and processed through specific activity patterns and synaptic plasticity. Here, we introduce "EVOX," an advanced network electrophysiology platform equipped with high-density microelectrode arrays to capture critical network-level synaptic dynamics integral to learning and memory. This platform surpasses traditional methods by enabling label-free, high-order mapping of neural interactions, providing unprecedented insights into network Long-Term Potentiation (LTP) and evoked synaptic transmission within the hippocampal network. Utilizing EVOX, we demonstrate that high-frequency stimulation induces network-wide LTP, revealing enhanced synaptic efficacy in previously inactive cell assemblies in hippocampal layers. Our platform enables the real-time observation of network synaptic transmission, capturing the intricate patterns of connectivity and plasticity that underpin memory encoding. Advanced computational techniques further elucidate the mesoscale transmembrane generators and the dynamic processes that govern network-level memory encoding mechanisms. These findings uncover the complex dynamics that underlie learning and memory, showcasing EVOXs potential to explore synaptic and cellular phenomena in aging circuits. EVOX not only advances our understanding of hippocampal memory mechanisms but also serves as a powerful tool to investigate the broader scope of neural plasticity and network interactions in healthy and diseased states.

neuroscience↗

bioRxiv2024

MEA-seqX: High-resolution Profiling of Large-scale Electrophysiological and Transcriptional Network Dynamics

Concepts of brain function imply congruence and mutual causal influence between molecular events and neuronal activity. Decoding entangled information from concurrent molecular and electrophysiological network events demands innovative methodology bridging scales and modalities. Our MEA-seqX platform, integrating high-density microelectrode arrays, spatial transcriptomics, optical imaging, and advanced computational strategies, enables the simultaneous recording and analysis of molecular and electrical network activities at the level of individual cells. Applied to a mouse hippocampal model of experience-dependent plasticity, MEA-seqX unveiled massively enhanced nested dynamics between transcription and function. Graph-theoretic analysis revealed an increase in densely connected bimodal hubs, marking the first observation of coordinated spatiotemporal dynamics in hippocampal circuitry at both molecular and functional levels. This platform also identified different cell types based on their distinct bimodal profiles. Machine-learning algorithms accurately predicted network-wide electrophysiological features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.

neuroscience↗

bioRxiv2022

Rich Experience Boosts Functional Connectome and High-Dimensional Coding in Hippocampal Network

Challenging the brain with experiential richness creates tissue-level changes and synaptic plasticity, but the interjacent network level has not been accessible. We here show that environmental enrichment has unexpectedly far-reaching effects on network connectivity and multi-dimensional coding in the hippocampus. We present direct evidence that experience impacts local and global network connectivity, synchrony, and rhythmic dynamics. For this, we investigated the hippocampi from standard-housed mice (SD) and mice living in an enriched environment (ENR) using large-scale ex vivo recordings with a high-density microelectrode sensing array that - with the unprecedented spatiotemporal resolution-allowed simultaneous electrophysiological assessment across the entire circuit. In the absence of extrinsic electrical network stimulation, we found enhanced functional connectivity and high-dimensional coding in hippocampal-cortical networks of ENR mice. The mapped connectome illustrated a scale-free smallworld topology and an ENR-induced resilience to random failures. ENR enhanced large-scale spatiotemporal firing patterns, which facilitated efficient pattern separation and boosted the information encoded in the firing phases of slow oscillatory rhythms. Given that essentially all electrophysiological studies on network behaviors have been done on animals housed in stimulus-poor conditions, our SD mice showed the expected normal functionality. The literature consequently underestimates the extent of spontaneous network activity and connectivity under truly physiological conditions. Our results pave the way to unveil fundamental mechanisms of experience-dependent enhancement in the hippocampal network underlying high brain functions and provide markers for large-scale network remodeling and metaplasticity.

neuroscience↗

bioRxiv2021

Revealing Spatiotemporal Circuit Information of Olfactory Bulb in Large-scale Neural Recordings

Large-scale multi-site biosensors are essential to probe the olfactory bulb (OB) circuitry for understanding the spatiotemporal dynamics of simultaneous discharge patterns. Current ex-vivo electrophysiological techniques are limited to recording a small set of neurons and cannot provide an inadequate resolution, which hinders revealing the fast dynamic underlying the information coding mechanisms in the OB circuit. Here, we demonstrate a novel biohybrid OB-CMOS platform to decipher the cross-scale dynamics of OB electrogenesis and quantify the distinct neuronal coding properties. The approach with 4096-microelectrodes offers a non-invasive, label-free, bioelectrical imaging to decode simultaneous firing patterns from thousands of connected neuronal ensembles in acute OB slices. The platform can measure spontaneous and drug-induced extracellular field potential activity. We employ our OB-CMOS recordings to perform multidimensional analysis to instantiate specific neurophysiological metrics underlying the olfactory spatiotemporal coding that emerged from the OB interconnected layers. Our results delineate the computational implications of large-scale activity patterns in functional olfactory processing. The high-content characterization of the olfactory circuit could benefit better functional interrogations of the olfactory spatiotemporal coding, connectivity mapping, and, further, the designing of reliable and advanced olfactory cell-based biosensors for diagnostic biomarkers and drug discovery.

neuroscience↗