High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity

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. The 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 mesoscale spatial resolution. Applied to a mouse hippocampal model of experience-dependent plasticity, MEA-seqX unveils massively enhanced nested dynamics between transcription and function. Graph-theoretic analysis reveals an increase in densely connected bimodal hubs, marking the first observation of coordinated hippocampal circuitry dynamics at molecular and functional levels. This platform also identifies different cell types based on their distinct bimodal profiles. Machine-learning algorithms accurately predict network-wide electrophysiological activity features from spatial gene expression, demonstrating a previously inaccessible convergence across modalities, time, and scales.

Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.

Beyond nature, nurture, and chance: Individual agency shapes divergent learning biographies and brain connectome

Individual choices shape life course trajectories of brain structure and function beyond genes and environment. We hypothesized that individual task engagement in response to a learning program results in individualized learning biographies and connectomics. Genetically identical female mice living in one large shared enclosure freely engaged in self-paced, automatically administered and monitored learning tasks. We discovered growing and increasingly stable interindividual differences in learning trajectories. Adult hippocampal neurogenesis and connectivity as assessed by a high-density multielectrode array positively correlated with the variation in exploration and learning efficiency. During some tasks, divergence transiently collapsed, highlighting the sustained significance of context for individualization. Thus, equal environments and equal genes do not result in equal learning biographies because life confronts individuals with choices that lead to divergent paths.

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 DENOISING's 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.

Novel Multiparametric Bioelectronic Measurement System for Monitoring Virus-Induced Alterations in Functional Neuronal Networks

Development and optimisation of bioelectronic monitoring techniques like microelectrode array-based field potential measurement and impedance spectroscopy for the functional, label-free and non-invasive monitoring of in vitro neuronal networks is widely investigated in the field of biosensors. Thus, these techniques were individually used to demonstrate the capabilities of, e.g., detecting compound-induced toxicity in neuronal culture models. In contrast, extended application for investigating the effects of central nervous system infecting viruses are rarely described. In this context, we wanted to analyse the effect of herpesviruses on functional neuronal networks. Therefore, we developed a unique hybrid bioelectronic monitoring platform that allows for performing field potential monitoring and impedance spectroscopy on the same microelectrode. In the first step, a neuronal culture model based on primary hippocampal cells from neonatal rats was established with reproducible and stable synchronised electrophysiological network activity after 21 days of cultivation on microelectrode arrays. For a proof of concept, the pseudorabies model virus PrV Kaplan-ΔgG-GFP was applied and the effect on the neuronal networks was monitored by impedance spectroscopy and field potential measurement for 72 h in a multiparametric mode. Analysis of several bioelectronic parameters revealed a virus concentration-dependent degeneration of the neuronal network within 24-48 h, with a significant early change in electrophysiological activity, subsequently leading to a loss of activity and network synchronicity. In conclusion, we successfully developed a microelectrode array-based hybrid bioelectronic measurement platform for quantitative monitoring of pathologic effects of a herpesvirus on electrophysiological active neuronal networks.

Novel high-dense microelectrode array based multimodal bioelectronic monitoring system for cardiac arrhythmia re-entry analysis

In recent decades, significant progress has been made in the treatment of heart diseases, particularly in the field of personalized medicine. Despite the development of genetic tests, phenotyping and risk stratification are performed based on clinical findings and invasive in vivo techniques, such as stimulation conduction mapping techniques and programmed ventricular pacing. Consequently, label-free non-invasive in vitro functional analysis systems are urgently needed for more accurate and effective in vitro risk stratification, model-based therapy planning, and clinical safety profile evaluation of drugs. To overcome these limitations, a novel multilayer high-density microelectrode array (HD-MEA), with an optimized configuration of 512 sensing and 4 pacing electrodes on a sensor area of 100 mm2, was developed for the bioelectronic detection of re-entry arrhythmia patterns. Together with a co-developed front-end, we monitored label-free and in parallel cardiac electrophysiology based on field potential monitoring and mechanical contraction using impedance spectroscopy at the same microelectrode. In proof of principle experiments, human induced pluripotent stem cell (hiPS)-derived cardiomyocytes were cultured on HD-MEAs and used to demonstrate the sensitive quantification of contraction strength modulation by cardioactive drugs such as blebbistatin (IC50 = 4.2 μM), omecamtiv and levosimendan. Strikingly, arrhythmia-typical rotor patterns (re-entry) can be induced by optimized electrical stimulation sequences and detected with high spatial resolution. Therefore, we provide a novel cardiac re-entry analysis system as a promising reference point for diagnostic approaches based on in vitro assays using patient-specific hiPS-derived cardiomyocytes.

Assessment of chemotherapeutic effects on cancer cells using adhesion noise spectroscopy

With cancer as one of the leading causes of death worldwide, there is a need for the development of accurate, cost-effective, easy-to-use, and fast drug-testing assays. While the NCI 60 cell-line screening as the gold standard is based on a colorimetric assay, monitoring cells electrically constitutes a label-free and non-invasive tool to assess the cytotoxic effects of a chemotherapeutic treatment on cancer cells. For decades, impedance-based cellular assays extensively investigated various cell characteristics affected by drug treatment but lack spatiotemporal resolution. With progress in microelectrode fabrication, high-density Complementary Metal Oxide Semiconductor (CMOS)-based microelectrode arrays (MEAs) with subcellular resolution and time-continuous recording capability emerged as a potent alternative. In this article, we present a new cell adhesion noise (CAN)-based electrical imaging technique to expand CMOS MEA cell-biology applications: CAN spectroscopy enables drug screening quantification with single-cell spatial resolution. The chemotherapeutic agent 5-Fluorouracil exerts a cytotoxic effect on colorectal cancer (CRC) cells hampering cell proliferation and lowering cell viability. For proof-of-concept, we found sufficient accuracy and reproducibility for CAN spectroscopy compared to a commercially available standard colorimetric biological assay. This label-free, non-invasive, and fast electrical imaging technique complements standardized cancer screening methods with significant advances over established impedance-based approaches.

Recent strategies for neural dynamics observation at a larger scale and wider scope

The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.