Journal of neural engineering最新文献

{"title":"Feature fusion based on global-local weighted attention model for automatic epileptic seizure detection.","authors":"Xiang Li, Ke Zhang, Xin Wang, Zhiheng Zhang, Pengsheng Zhu, Mingxing Zhu, Xianhai Zeng, Shixiong Chen","doi":"10.1088/1741-2552/ae00f4","DOIUrl":"10.1088/1741-2552/ae00f4","url":null,"abstract":"<p><p><i>Objective</i>. Epilepsy is a neurological disorder characterized by recurrent seizures, which present significant challenges in both diagnosis and treatment. Despite advances in seizure detection, existing methods often struggle with accurately capturing the complex and dynamic interactions between temporal, spatial, and spectral features of electroencephalography (EEG) signals. This leads to limitations in the detection accuracy and generalization across different datasets.<i>Approach</i>. To address these challenges, we propose global-local weighted attention (GLWA) model, which integrates temporal, spatial, and spectral features through a local-global attention mechanism. At the same time, GLWA effectively balances both global and local features, capturing comprehensive information from EEG signals to enhance seizure detection accuracy.<i>Main results</i>. Our proposed model achieves accuracy rates of 98.82% and 98.89% on the CHB-MIT and Siena datasets, respectively. These results demonstrate the model's capability to effectively integrate these features, resulting in improved detection performance.<i>Significance</i>. Furthermore, we visualize the model's decision-making process to gain insights into the attention distribution across different brain regions and spectraluency bands, further emphasizing GLWA's potential in seizure detection. This work demonstrates the model's superior performance and interpretability, providing a robust approach for accurate and generalizable identification of seizures.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review of experimental studies in humans on transcranial temporal interference stimulation.","authors":"Paria Mansourinezhad, Rob M C Mestrom, Debby C W Klooster, Mathieu Sprengers, Paul A J M Boon, Margarethus M Paulides","doi":"10.1088/1741-2552/ae0524","DOIUrl":"10.1088/1741-2552/ae0524","url":null,"abstract":"<p><p>Transcranial temporal interference stimulation (tTIS) has recently emerged as a non-invasive neuromodulation method aimed at reaching deeper brain regions than conventional techniques. However, many questions about its effects remain, requiring further experimental studies. This review consolidates the experimental literature on tTIS's effects in the human brain, clarifies existing evidence, identifies knowledge gaps, and proposes future research directions to evaluate its potential. A systematic literature search was performed in PubMed, Web of Science, and Scopus for studies published up to 27 January 2025. Eligible studies applied tTIS to the human brain and examined its effects on neural, behavioral, and clinical outcomes. Of 127 publications screened, 18 met the inclusion criteria. Studies were analyzed for design, stimulation target, parameters, control conditions, and outcome measures. Included studies exhibited low bias or minor concerns using the Cochrane RoB2 and ROBINS-I tools. Ten studies targeted cortical regions (motor, occipito-parietal, fronto-parietal), and eight probed subcortical sites (striatum, hippocampus, globus pallidus, caudate). Motor-cortex tTIS enhanced motor-network connectivity, though the effect was similar to that of transcranial direct current stimulation. Beta-band stimulation envelopes (20 Hz) promoted learning-related plasticity, while gamma-band envelopes (70 Hz) yielded immediate performance improvements. Occipito-parietal tTIS did not modulate alpha power. Preliminary deep-target findings are promising: 5 Hz hippocampal tTIS improved episodic recall, 100 Hz striatal tTIS enhanced motor learning in older adults, and 100 Hz hippocampal-entorhinal tTIS aided spatial navigation. Two fMRI studies confirmed network-specific modulation, although one raised concerns about using a fixed montage between individuals. Clinical evidence remains limited, with two Parkinson's pilots and one epilepsy study showing short-term benefits. Overall, tTIS shows potential to modulate human brain activity and behavior. However, current evidence is preliminary and predominantly focused on cortical rather than deep targets. Larger, well-controlled studies are needed to reliably determine whether tTIS can effectively engage subcortical structures and provide meaningful clinical benefits.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motor unit number estimation based on convolutional neural network.","authors":"Junjun Chen, ZeZhou Li, Linyan Wu, Zhiyuan Lu, Maoqi Chen, Ping Zhou","doi":"10.1088/1741-2552/ae01da","DOIUrl":"10.1088/1741-2552/ae01da","url":null,"abstract":"<p><p><i>Objective</i>. The compound muscle action potential (CMAP) scan contains a muscle's detailed stimulus-activation information and thereby can be used for motor unit number estimation (MUNE). Due to the challenges in accurately obtaining the motor unit numbers from experimental CMAP scans, most existing MUNE methods rely on data fitting, which is time-consuming and requires manual operations. This study explored the feasibility of a neural network-based MUNE approach and proposes an end-to-end model for rapid estimation.<i>Approach</i>. We developed NNEstimation, a novel supervised learning framework based on a convolutional neural network, to estimate motor unit numbers from both synthetic and experimental CMAP scans. A probabilistic model with varied parameters was used to generate CMAP scans with diverse characteristics for neural network training. NNEstimation trained on synthetic data was directly tested on both synthetic and experimental data.<i>Main results</i>. Evaluations on synthetic CMAP scans demonstrate that NNEstimation achieves lower estimation error and shorter execution time than the conventional data fitting method, MScanFit. The accuracy of NNEstimation is influenced by motor unit numbers of CMAP scans and remains unaffected by noise levels, recorded amplitudes, or motor unit activation thresholds. Moreover, NNEstimation's estimates on experimental data are highly consistent with those of MScanFit.<i>Significance</i>. Although trained solely on synthetic CMAP scans, NNEstimation achieves estimation results comparable to those of the traditional algorithm on experimental data while significantly reducing execution time, demonstrating its potential for practical MUNE applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.","authors":"Michael Joseph Del Sesto, Serban Negoita, Maria Bruzzone Giraldez, Zachary LaJoie, Khaleda Akhter Sathi, Joshua K Wong, Alik S Widge, Michael S Okun, Adam Khalifa","doi":"10.1088/1741-2552/ae08ea","DOIUrl":"10.1088/1741-2552/ae08ea","url":null,"abstract":"<p><p>Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used by therapeutic and systems neuroscience applications. However, the broad clinical adoption of invasive distributed deep brain interfaces remains in its early stages. This review explores the barriers to implementation by addressing three key questions: Do the benefits of implanting multiple electrodes justify the associated risks for specific applications? What is the risk-benefit ratio, and what technological advancements will be necessary to encourage clinical adoption? We also examine next-generation technologies that could enable distributed brain interfaces, including system-on-chip micro-stimulators as well as nanoparticles. We highlight the role of novel machine learning algorithms in the optimization of stimulation parameters and for the guidance of device placement. Emerging hardware accelerators equipped with on-chip AI have demonstrated functionality that can be used to decode and to classify distributed neuronal data. This advance in hardware accelerators has also contributed to the potential for enhanced closed-loop stimulation control of devices. Despite these advances, significant technological and translational barriers persist, limiting the widespread clinical application of distributed brain interfaces. This review provides a critical analysis of recent prototypes and novel hardware for use in distributed systems. We will discuss both clinical and research applications. We will focus and highlight the utilization of multi-site technologies to meet the needs of neurological diseases. We conclude that there exists a critical need for further innovation and integration of multi-site technologies into clinical practice.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disrupted periodic spatiotemporal pattern and dynamic reorganization of its basic states in generalized epilepsy.","authors":"Junxia Chen, Sisi Jiang, Guofeng Ye, Zhihuan Yang, Changyue Hou, Hechun Li, Haonan Pei, Roberto Rodriguez-Labrada, Jianfu Li, Dezhong Yao, Cheng Luo","doi":"10.1088/1741-2552/ae01d9","DOIUrl":"10.1088/1741-2552/ae01d9","url":null,"abstract":"<p><p><i>Objective.</i>The anti-correlation between the default mode network (DMN) and task-positive network (TPN) is a stable characteristic of normal brain activity. However, in idiopathic generalized epilepsy (IGE), this anti-correlation is often disrupted and strongly associated with epileptic seizures. This study aims to use periodic spatiotemporal patterns (PSTP) analysis to elucidate the relationship between the DMN-TPN anti-correlation and epileptic activity, providing new insights into the neural mechanisms underlying IGE.<i>Approach.</i>Resting-state functional magnetic resonance imaging was used to analyze PSTP in both healthy controls and IGE patients. A pattern-finding algorithm was initially applied to identify repeated spatiotemporal patterns, followed by a novel PSTP-finding algorithm to uncover dynamic periodic patterns through analysis of fluctuations in the DMN-TPN anti-correlation. The Hilbert transform was applied to capture the underlying basic states of these periodic patterns. Additionally, the relationship between period length and intrinsic neural timescales (INT) was explored.<i>Main results.</i>IGE patients exhibited a reduced DMN-TPN anti-correlation, particularly during TPN-dominant states. Additionally, IGE patients exhibited greater dynamic instability in basic states, marked by more frequent transitions between transitional states. Furthermore, lower correlations between period length and INT were observed in cognitive regions of IGE patients.<i>Significance</i>. These findings suggest that dynamic switching between the DMN and TPN in IGE is weaker and less balanced, with disruptions in periodic rhythms linked to cognitive impairments. The proposed PSTP framework provides new insights into the abnormal rhythms of IGE from a spatiotemporal perspective.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closed-form expressions for the directions of maximum modulation depth in temporal interference electrical brain stimulation.","authors":"Mariano Fernández-Corazza, Sergei Turovets, Carlos H Muravchik","doi":"10.1088/1741-2552/ae01dc","DOIUrl":"10.1088/1741-2552/ae01dc","url":null,"abstract":"<p><p><i>Objective.</i>In temporal interference (TI) transcranial electrical stimulation (tES), an emerging brain stimulation technique, the interference of two high-frequency currents with a small frequency difference is used to target specific brain regions with better focality than in standard tES. While the magnitude of the modulation depth has been previously investigated, an explicit formula for the direction in which this modulation is maximized has been lacking. This work provides a novel closed-form analytical expression for the orientation of maximum modulation depth in TI tES. We also found a secondary orientation where the modulation depth has a local maximum. Moreover, we provide closed-form analytical formulas for this orientation as well as for the modulation depth along this orientation. To our knowledge, these closed-form expressions and the presence of the secondary maximum have not been previously reported.<i>Approach.</i>We derive compact analytical expressions and validate them through comprehensive computational simulations using a realistic human head model. We also provide a complete analytical derivation of the widely used formula for the maximum modulation depth magnitude stated in Grossman et al, 2017.<i>Main results.</i>Our simulations demonstrate that the modulation depth predicted with our new analytical direction formula is indeed the maximum compared to other directions. The derived closed-form expression provides a faster and more accurate alternative to iterative numerical optimization methods used in previous studies to estimate this direction. Furthermore, we found that due to interference in 3D, the modulation depth along the secondary maximum orientation can be of similar strength to the maximum modulation depth intensity when interfering electric field vectors are significantly misaligned. Finally, we show that by modifying the ratio of the injected current strengths, it is possible to steer these directions and fine-tune the stimulation along a desired direction of interest.<i>Significance.</i>Overall, this work provides a detailed treatment of TI electric fields in 3D. The presented closed-form expressions for the directions of maximum and secondary maximum modulation depths are relevant for the better interpretation of both simulated and experimental results in TI studies by allowing comparison with neuronal orientations in the brain.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal basis function models for closed-loop neural stimulation.","authors":"Matthew J Bryan, Felix Schwock, Azadeh Yazdan-Shahmorad, Rajesh P N Rao","doi":"10.1088/1741-2552/ae036a","DOIUrl":"10.1088/1741-2552/ae036a","url":null,"abstract":"<p><p><i>Objective.</i>Closed-loop neural stimulation provides novel therapies for neurological diseases such as Parkinson's disease (PD), but it is not yet clear whether artificial intelligence (AI) techniques can tailor closed-loop stimulation to individual patients or identify new therapies. Further advancements are required to address a number of difficulties with translating AI to this domain, including sample efficiency, training time, and minimizing loop latency such that stimulation may be shaped in response to changing brain activity.<i>Approach.</i>We propose temporal basis function models (TBFMs) to address these difficulties, and explore this approach in the context of excitatory optogenetic stimulation. We demonstrate the ability of TBF models to provide a single-trial, spatiotemporal forward prediction of the effect of optogenetic stimulation on local field potentials measured in two non-human primates. The simplicity of TBF models allow them to be sample efficient (<20 min of training data), rapid to train (<5 min), and low latency (<0.2 ms) on desktop CPUs.<i>Main results.</i>We demonstrate the model on 40 sessions of previously published excitatory optogenetic stimulation data. Surprisingly, on test sets it achieved a prediction accuracy 44% higher than a complex nonlinear dynamical systems model that requires hours to train, and 158% higher than a linear state-space model requiring 90 min to train. Additionally, in two simulations we show that it successfully allows a closed-loop stimulator to drive neural trajectories, and to achieve the user-preferred trade-offs between under- and over-stimulation, given the uncertainty in the model; it achieves an area under curve of ∼0.7 in both cases.<i>Significance.</i>By optimizing for sample efficiency, training time, and latency, our approach begins to bridge the gap between complex AI-based approaches to modeling dynamical systems and the vision of using such forward prediction models to develop novel, clinically useful closed-loop stimulation protocols.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145002379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ICA-based artifact suppression method for online extraction of TMS-evoked potentials: toward closed-loop TMS-EEG applications beyond the motor cortex.","authors":"Sina Varmaghani, Ronald Phlypo, Olivier David, Sylvain Harquel, Alan Chauvin","doi":"10.1088/1741-2552/ae01d8","DOIUrl":"10.1088/1741-2552/ae01d8","url":null,"abstract":"<p><p><i>Objective.</i>Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) has become a valuable tool in clinical and cognitive neuroscience. However, TMS-EEG signals often suffer from severe artifacts, particularly in lateral cortical regions where TMS-evoked muscle artifacts are pronounced, making real-time recovery of TMS-evoked potentials (TEPs) challenging. We developed and validated a real-time, two-step independent component analysis (ICA)-based artifact cleaning method for TMS-EEG signals, facilitating the rapid extraction of clean neural signals for closed-loop neurostimulation applications.<i>Approach.</i>Our method involves an offline ICA training phase, where ICA weights and artifact topographies are identified using pre-experimental trials, followed by an online phase in which the precomputed weight matrices are applied in real-time to incoming data. We conducted simulations on two pre-published TMS-EEG datasets (<i>N</i>= 28, ROIs = 6) to validate the method by identifying the minimum number of trials required to estimate ICA weights. We also assessed the reproducibility of TEPs and the stability of ICA components, taking classical offline TEPs as the relative ground truth.<i>Main Results.</i>ICA analysis suggests that it can be applied reliably within each region without significant loss of convergence and stability, provided careful consideration is given to the size and composition of the data used for ICA training. Simulation results indicated that while central regions could achieve reliable TEPs similar to ground truth with as few as 20-30 trials to train ICA in the pre-experimental phase, frontal and occipital regions required 50-60 trials to reach a comparable level of reliability. Later TEP peaks (>100 ms) in all regions achieved high reproducibility when at least 35 training trials were used, whereas earlier peaks (<80 ms) showed moderate reproducibility with the same number of trials.<i>Significance.</i>These findings establish the feasibility and proof-of-concept for real-time ICA-based artifact removal for closed-loop TMS-EEG applications. The method enables rapid extraction of clean neural signals, allowing adaptation of stimulation parameters in real time, thereby facilitating individualized neurostimulation paradigms.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized beta band HD-tACS over the left SMA improves speech and limb movement by modulating prefrontal delta oscillations in neurotypical young adults.","authors":"Fatemeh Tabari, Joel Isaac Berger, Melda Kunduk, Arend W A Van Gemmert, Karim Johari","doi":"10.1088/1741-2552/ae00f5","DOIUrl":"10.1088/1741-2552/ae00f5","url":null,"abstract":"<p><p><i>Objective</i>. The supplementary motor area (SMA) demonstrates abnormal beta activity (13-30 Hz) during speech and limb movement tasks in neurological conditions such as Parkinson's disease (PD). Transcranial alternating current stimulation (tACS) has demonstrated promising improvement in motor and non-motor functions by entraining endogenous neural oscillations. We conducted an exploratory study on the modulatory effects of personalized beta high-definition (HD)-tACS over the left SMA on speech production and limb movement.<i>Approach.</i>In a repeated-measures experiment, twenty-two neurotypical young adults were recruited to participate in four stimulation conditions: sham, HD transcranial random noise stimulation (HD-tRNS), and HD-tACS tuned to each individual's frequency of maximal SMA beta activity (identified using source-localized EEG) during speech (tuned-to-speech, TtS) and limb movement (tuned-to-limb, TtL). All participants completed a 25 min sham/active stimulation over the left SMA, followed by an interleaved speech production and limb movement task.<i>Main results</i>. Behavioral results showed that active stimulation resulted in more pronounced improvements in reaction times compared to the sham condition, regardless of the active stimulation type. The neural correlates of this aftereffect were indicated by a prominent modulation in delta power in prefrontal and frontocentral electrodes during speech and limb movement tasks following personalized beta TtS and TtL HD-tACS, relative to sham and tRNS.<i>Significance</i>. Personalized beta HD-tACS modulated delta oscillations, rather than beta rhythms, in a task-specific manner, highlighting the brain's adaptive response. These findings have implications for neurological conditions such as PD, which are characterized by deficits in speech production and limb motor coordination.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inhibiting JNK and PI3K-Akt signaling pathways altered spontaneous network bursts and developmental trajectories of neuronal networks.","authors":"Xiaoli Jia, Qiuyan Zhu, Hailin Lu, Zhihong Zhou, Tahir Ali, Shupeng Li, Jinxing Feng","doi":"10.1088/1741-2552/ae01db","DOIUrl":"10.1088/1741-2552/ae01db","url":null,"abstract":"<p><p><i>Objective.</i>Spontaneous network bursts (NBs) are critical for neuronal circuit development, influencing synaptogenesis and functional organization. While JNK and PI3K-Akt signaling pathways are known to regulate synaptic plasticity, their specific roles in governing NBs dynamics and functional network organization remain poorly understood. This study investigates the roles of JNK and PI3K-Akt signaling in regulating spontaneous NBs dynamics and network organization in cultured neuronal networks.<i>Approach.</i>Using longitudinal microelectrode array (MEA) recordings from cultured cortical neurons (DIV14-49), we pharmacologically inhibited JNK (SP600125, JNK-IN-8) and PI3K-Akt (LY294002, GDC-0941) pathways. We quantitatively analyzed NBs profiles (maximum firing rate/MFR, burst length/BL, rising phase/RP) and functional network properties (modularity, betweenness centrality) during development.<i>Main results.</i>JNK inhibition increased MFR but reduced RP and FP, and decreased betweenness centrality and network modularity, particularly in DIV21. PI3K-Akt inhibition caused delayed effects: decreased MFR at DIV49 with increased RP, while enhancing network modularity. Developmental analysis revealed a transition from core-node-driven NBs (strong MFR-betweenness and BL-betweenness correlation at DIV14) to modularly organized NBs (strong BL-modularity and MFR-modularity correlation at DIV49), with pathway inhibitors differentially altering these relationships.<i>Significance.</i>Our findings demonstrate that JNK and PI3K-Akt pathways play distinct temporal roles in regulating NBs dynamics and network organization. JNK signaling is crucial for maintaining early core-node functionality, whereas PI3K-Akt signaling promotes the development of mature modular architecture. Our findings enhance the understanding of how molecular signaling influences neuronal network dynamics, contributing to a broader framework for studying neurodevelopmental principles.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144983793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}