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Biomedicines
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Neuroscience Through Electrophysiology: Current Trends and Future Directions
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Neuroscience Through Electrophysiology: Current Trends and Future Directions

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Neurobiology and Clinical Neuroscience".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2819

Special Issue Editor

Dr. Xiaokuang Ma

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Guest Editor
Basic Medical Sciences, College of Medicine-Phoenix, The University of Arizona, Phoenix, AZ 85004, USA
Interests: neuroscience; aging; neuroimaging; neurodevelopmental disorders; electrophysiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrophysiology has long served as a cornerstone of neuroscience, offering unparalleled insights into the dynamic electrical activity that underpins brain function. By measuring voltage changes and ionic currents across neuronal membranes, electrophysiological techniques—from single-channel recordings to large-scale electroencephalography (EEG)—have decoded the mechanisms of action potentials, synaptic transmission, and network oscillations. Pioneering work, such as the Hodgkin–Huxley model derived from voltage-clamp studies, laid the foundation for understanding neural excitability, while intracellular and extracellular recordings have mapped the connectivity and plasticity of neural circuits. Clinically, electrophysiology has been indispensable in diagnosing disorders like epilepsy and Parkinson’s disease, linking aberrant electrical patterns to pathological states.

This Special Issue highlights the latest advancements in electrophysiological methods and their applications in modern neuroscience. The collection covers a wide range of topics, including innovations in in vitro patch-clamp whole-cell recording, high-density multi-electrode arrays, the optogenetic modulation of neural circuits, and the integration of electrophysiology with neuroimaging and computational modeling. These approaches provide deeper insights into neural circuit function, synaptic plasticity, and the pathophysiology of neurodevelopmental and neurodegenerative disorders.

Looking ahead, the combination of electrophysiology with multimodal imaging, genomics, and bioelectronic interfaces holds the potential for transformative breakthroughs. Furthermore, the Issue explores the future trajectory of electrophysiology, focusing on emerging technologies such as brain–machine interfaces, artificial intelligence-driven data analysis, and non-invasive electrophysiological techniques for clinical applications. This discussion highlights electrophysiology’s translational potential, which includes enhancing brain–machine interfaces and personalizing treatments for neuropsychiatric conditions.

Collectively, this Special Issue aims to foster interdisciplinary collaborations and drive new discoveries in neuroscience. We invite researchers to contribute their findings, helping to shape the future of electrophysiology and its role in understanding and treating neurological and psychiatric disorders.

Dr. Xiaokuang Ma
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrophysiology
  • neural circuit
  • synaptic plasticity
  • neuronal membrane
  • patch-clamp
  • action potential
  • ion channels
  • neurodevelopmental disorders
  • neurodegenerative disorders

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Published Papers (3 papers)

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Research

12 pages, 1144 KB  
Article
A Retrospective Study on Correlations Between EEG Signals (N20, Spectral Entropy, and Alpha Variability) and Prognosis of Traumatic Brain Injury
by Xia Liu, Mengxu Qiao, Qi Liu, Meilin Ai, Jing Deng, Jian Wang, Haojun Yang and Li Huang
Biomedicines 2026, 14(5), 1033; https://doi.org/10.3390/biomedicines14051033 - 1 May 2026
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Abstract
Aim: To observe the correlations between electroencephalography (EEG) signals and clinical outcomes in patients with traumatic brain injury (TBI). Methods: A total of 174 patients diagnosed with TBI at Xiangya Hospital during January 2017 and June 2024 were included in this study. Quantitative [...] Read more.
Aim: To observe the correlations between electroencephalography (EEG) signals and clinical outcomes in patients with traumatic brain injury (TBI). Methods: A total of 174 patients diagnosed with TBI at Xiangya Hospital during January 2017 and June 2024 were included in this study. Quantitative EEG parameters, including spectral entropy (SE), alpha variability (RAV), and relative spectral energy (RBP), along with somatosensory evoked potential (SSEP) recordings (N20 amplitude) were assessed within 7–14 days after the disease onset. Patients were divided into a good-prognosis group and a poor-prognosis group based on the Glasgow Outcome Scale (GOS) scores at six months after discharge. Results: Significant correlations were found between the initial Synek EEG grading and 6-month GOS score (ρ = −0.709, p < 0.001). Compared with patients in the poor-prognosis group, significantly higher N20 amplitudes (p < 0.001), higher SE (p = 0.049), higher RAV (p = 0.009), and lower relative beta energy (p < 0.05) were found in TBI patients with good prognosis. Among these parameters, N20 amplitude demonstrated the best predictive performance. The N20 amplitude threshold of >1.975 μV predicted a good outcome with a sensitivity of 93.3% and a specificity of 94.1%. Conclusions: These findings may provide a reliable and sensitivity method to evaluate and predict the prognosis of TBI patients, which has important clinical management significance. Full article
(This article belongs to the Special Issue Neuroscience Through Electrophysiology: Current Trends and Future Directions)
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32 pages, 6451 KB  
Article
A Fast Synaptic Parameter Estimation Method Based on First- and Second-Order Moments for Short-Term Facilitating Synapses
by Jingyi Zhang, Tianyu Li, Xiaohui Zhang and Liber T. Hua
Biomedicines 2026, 14(4), 771; https://doi.org/10.3390/biomedicines14040771 - 28 Mar 2026
Viewed by 409
Abstract
Background: Short-term facilitation (STF) is a key form of synaptic plasticity driven by activity-dependent increases in presynaptic release probability. However, estimating core synaptic parameters—quantal size (q), vesicle pool size (N), and release probability (pi)—remains challenging [...] Read more.
Background: Short-term facilitation (STF) is a key form of synaptic plasticity driven by activity-dependent increases in presynaptic release probability. However, estimating core synaptic parameters—quantal size (q), vesicle pool size (N), and release probability (pi)—remains challenging due to nonlinear dynamics and unobservable presynaptic states, limiting the applicability of conventional methods. Methods: We developed a fast analytical framework based on first- and second-order statistical moments of evoked EPSCs, including mean, variance, and cross-stimulus covariance. By constructing composite moment relationships, latent variables were algebraically eliminated, yielding closed-form estimators of synaptic parameters. To improve robustness under strong facilitation, a Tsodyks–Markram (T–M) model-based calibration step was introduced to refine N and pi using the estimated q as a constraint. Results: Applied to hippocampal CA3–CA1 synapses, the method produced accurate and stable estimates of q across varying noise and sampling conditions. Incorporation of cross-stimulus covariance enabled effective characterization of structured variability that is neglected in classical approaches. While direct estimates of N and pi showed dispersion, T–M calibration significantly improved stability and physiological consistency. Compared with mean–variance analysis, the proposed method achieved superior performance under facilitating conditions. Conclusions: This hybrid framework enables rapid and reliable estimation of synaptic parameters in STF synapses by exploiting second-order statistical structure. It provides a practical tool for investigating presynaptic mechanisms and may facilitate quantitative studies of synaptic dysfunction in neurological and psychiatric disorders. Full article
(This article belongs to the Special Issue Neuroscience Through Electrophysiology: Current Trends and Future Directions)
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15 pages, 2381 KB  
Article
Interhemispheric Functional Hypoconnectivity Is an Early Marker of Cortical Epileptogenesis
by Tatiana M. Medvedeva and Lyudmila V. Vinogradova
Biomedicines 2026, 14(3), 549; https://doi.org/10.3390/biomedicines14030549 - 28 Feb 2026
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Abstract
Background: Epilepsy is a network disorder, and network-based approaches to its diagnostics and therapies attract growing attention. Identification of prognostic markers of epileptogenesis and long-term risk for developing epilepsy after brain insults is an urgent, unresolved problem. We examined whether intracortical connectivity patterns [...] Read more.
Background: Epilepsy is a network disorder, and network-based approaches to its diagnostics and therapies attract growing attention. Identification of prognostic markers of epileptogenesis and long-term risk for developing epilepsy after brain insults is an urgent, unresolved problem. We examined whether intracortical connectivity patterns reflect early epileptogenic changes in the cortex. Methods: We used the audiogenic kindling model, in which cortical epileptogenesis is initiated by repetition of reflex subcortically-driven seizures. Two measures of functional connectivity—mutual information and mean phase coherence—were applied to electrocorticographic recordings obtained from homotopical sites of parietal cortex during interictal and immediate postictal periods in awake rats. Interhemispheric connectivity and synchrony in non-kindled and slightly kindled rats were compared. Cortical spreading depolarization (SD), the first manifestation of growing cortical excitability in several models of epileptogenesis, was used as an electrographic marker of the earliest kindling stage. Results: In kindled animals, baseline levels of hemispheric connectivity and gamma band synchrony were significantly lower compared to seizure-naive rats. Before kindling, subcortical seizures elicited mild postictal depression of cortical gamma oscillations without changes in interhemispheric functional connectivity. Early in kindling, seizures produced wideband postictal depression of cortical activity and a striking drop in hemispheric connectivity. Conclusions: Primary network alterations during epileptogenesis involve hemispheric decoupling and reduced synchronization, both sustained (between seizures) and transient (postictal). Breakdown of long-range intracortical communication may reflect homeostatic plasticity and an active attempt to restrict epileptogenic reorganization of neural networks. We think that resting-state hemispheric hypocoupling could be an early marker of epileptogenesis. Seizure-induced SD contributes to the generation of postictal events. Full article
(This article belongs to the Special Issue Neuroscience Through Electrophysiology: Current Trends and Future Directions)
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