Journal of neural engineering最新文献

{"title":"Hearing restoration by a low-weight power-efficient multichannel optogenetic cochlear implant system.","authors":"Lukasz Jablonski, Tamas Harczos, Bettina Wolf, Gerhard Hoch, Lakshay Khurana, Alexander Dieter, Lennart Roos, Roland Hessler, Suleman Ayub, Patrick Ruther, Tobias Moser","doi":"10.1088/1741-2552/adf00f","DOIUrl":"10.1088/1741-2552/adf00f","url":null,"abstract":"<p><p><i>Objective.</i>In case of deafness, electrical cochlear implants (eCIs) bypass dysfunctional or lost hair cells by direct stimulation of the auditory nerve. However, spectral selectivity of eCI sound coding is low as the wide current spread from each electrode activates large sets of neurons along the tonotopic axis of the cochlea. As light can be better confined in space, optical cochlear implants (oCIs) combined with cochlear optogenetics promise to overcome this shortcoming of eCIs. This requires appropriate sound processing and control of multiple microscale emitters.<i>Approach.</i>Here, we describe the development, characterisation, and application of a preclinical miniaturised low-weight and wireless LED-based multichannel oCI system for hearing restoration, and its comparison to its sister eCI system. We present exemplary implementation of these systems in behavioural studies on freely moving rats.<i>Main results.</i>The system, which weights 15 g, is 20 mm in diameter and 20 mm in height, performed for up to 8 h in behavioural experiments on freely moving rats proving its utility for cueing auditory tasks in deaf animals.<i>Significance.</i>The head-worn oCI system enabled deafened rats to perform a locomotion task in response to acoustic stimulation proving the concept of multichannel optogenetic hearing restoration in rodents. This paves the way for implementation in other species and development of future clinical oCI systems for improved hearing restoration.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644476","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":"Tissue response to deep brain stimulation electrodes: A review of animal and neurohistopathological studies.","authors":"Dorothy X Zhao, Alexander L Green, Erin K Purcell","doi":"10.1088/1741-2552/adf669","DOIUrl":"https://doi.org/10.1088/1741-2552/adf669","url":null,"abstract":"<p><strong>Objective: </strong>Deep brain stimulation (DBS) is a neuromodulation therapy widely used to treat various neurological and neuropsychiatric conditions, with thousands of patients undergoing the procedure every year. However, despite the immense improvement in quality of life that most patients experience after surgery, many questions still remain surrounding various elements of DBS, including how the brain tissue responds to DBS electrodes and how that interaction may affect the therapy. &#xD;Approach: In this review, we build off a previous neurohistopathological review to encompass studies up to present date. &#xD;Main results: We identified 33 cases with 63 electrodes from patients with various disease pathologies and DBS targets. We supplemented the findings with animal studies. &#xD;Significance: These studies can provide evidence where neurohistopathological studies have not been performed. They can also offer predictions to guide future neurohistopathological studies. Better understanding of the tissue response to DBS electrodes can contribute to improved clinical outcomes.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762878","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":"Speech mode classification from electrocorticography: transfer between electrodes and participants.","authors":"Aurélie de Borman, Benjamin Wittevrongel, Bob Van Dyck, Kato Van Rooy, Evelien Carrette, Alfred Meurs, Dirk Van Roost, Marc M Van Hulle","doi":"10.1088/1741-2552/adf2de","DOIUrl":"10.1088/1741-2552/adf2de","url":null,"abstract":"<p><p><i>Objective.</i>Speech brain-computer interfaces (BCIs) aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak.<i>Approach.</i>In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier.<i>Main results.</i>High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified.<i>Significance</i>Accurately detecting speech from brain signals is essential to prevent spurious outputs from a speech BCI and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692861","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":"Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.","authors":"Lin Meng, Hongbo Zhao, Mengmeng Dong, Qing Wang, Yu Shi, Deyu Wang, Xiaodong Zhu, Rui Xu, Dong Ming","doi":"10.1088/1741-2552/adeeca","DOIUrl":"10.1088/1741-2552/adeeca","url":null,"abstract":"<p><p><i>Objective</i>. The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults (OAs).<i>Approach.</i>We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched OA and 15 young adults completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.<i>Main results.</i>Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700 ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.<i>Significance.</i>The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621619","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":"Detecting single motor-unit activity in magnetomyography.","authors":"Nima Noury, Justus Marquetand, Stefan Hartwig, Thomas Middelmann, Philip Broser, Markus Siegel","doi":"10.1088/1741-2552/adeaeb","DOIUrl":"10.1088/1741-2552/adeaeb","url":null,"abstract":"<p><p><i>Objective.</i>The study of motor unit (MU) discharge patterns is critical for understanding the mechanisms underlying human motor behavior. Intramuscular electromyography (iEMG) allows direct study of MU activity but is invasive. Surface electromyography offers a non-invasive alternative, but with lower spatial resolution. Recent advances in optically pumped magnetometers (OPMs) have sparked interest in the magnetic counterpart of EMG, magnetomyography (MMG), as an additional non-contact modality to study the neuromuscular system. However, it remains unclear whether MMG signals recorded with superconducting quantum interference devices (SQUIDs) or OPMs can be used to directly detect individual MUs.<i>Approach.</i>We addressed this question in a proof-of-principle study in which we recorded MMG signals from the abductor digiti minimi muscle using SQUIDs and OPMs. Critically, we simultaneously recorded iEMG from the same muscle as the ground truth to cross-validate the findings from the non-invasive measurements.<i>Main results.</i>We found that invasively recorded MUs can be detected in simultaneously recorded SQUID and OPM MMG signals. Furthermore, we found that individual MUs can be extracted directly from SQUID and OPM MMG and, importantly, validated this finding using the simultaneous iEMG recordings. These results provide converging evidence that individual MU activity is accessible using non-contact MMG.<i>Significance.</i>We demonstrate for the first time that individual MU activity is observable in MMG, paving the way for future research on MU decomposition using MMG. Our findings highlight the potential of MMG as a non-contact modality to study muscle activity in health and disease.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556293","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":"Making brain-computer interfaces as reliable as muscles.","authors":"Jonathan R Wolpaw","doi":"10.1088/1741-2552/addd47","DOIUrl":"10.1088/1741-2552/addd47","url":null,"abstract":"<p><p><i>Objective.</i>While brain-computer interfaces (BCIs) can restore basic communication to people lacking muscle control, they cannot yet restore actions that require the extremely high reliability of natural (i.e. muscle-based) actions. Most BCI research focuses on neural engineering; it seeks to improve the measurement and analysis of brain signals. But neural engineering alone cannot make BCIs reliable.<i>Approach.</i>A BCI does not simply decode brain activity; it enables its user to acquire a skill that is produced not by nerves and muscles but rather by the BCI. Thus, BCI research should focus also on neuroscience; it should seek to develop BCI skills that emulate natural skills.<i>Main results.</i>A natural skill is produced by a network of neurons and synapses that may extend from cortex to spinal cord. This network has been given the name<i>heksor</i>, from the ancient Greek word<i>hexis</i>. A heksor changes through life; it modifies itself as needed to maintain the key features of its skill, the attributes that make the skill satisfactory. Heksors overlap; they share neurons and synapses. Through their concurrent changes, heksors keep neuronal and synaptic properties in a<i>negotiated equilibrium</i>that enables each to produce its skill satisfactorily. A BCI-based skill is produced by a<i>synthetic heksor</i>, a network of neurons, synapses, and software that produces a BCI-based skill and should change as needed to maintain the skill's key features.<i>Significance.</i>A synthetic heksor shares neurons and synapses with natural heksors. Like natural heksors, it can benefit from multimodal sensory feedback, using signals from multiple brain areas, and maintaining the skill's key features rather than all its details. A synthetic heksor also needs successful co-adaptation between its central nervous system and software components and successful integration into the negotiated equilibrium that heksors establish and maintain. With due attention to both neural engineering and neuroscience, BCIs could become as reliable as muscles.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164369","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":"A reliable and reproducible real-time access to sensorimotor rhythm with a small number of optically pumped magnetometers.","authors":"Nikita Fedosov, Daria Medvedeva, Oleg Shevtsov, Alexei Ossadtchi","doi":"10.1088/1741-2552/aded35","DOIUrl":"10.1088/1741-2552/aded35","url":null,"abstract":"<p><p><i>Objective.</i>Recent advances in biomagnetic sensing have led to the development of compact, wearable devices capable of detecting weak magnetic fields generated by biological activity. Optically pumped magnetometers (OPMs) have shown significant promise in functional neuroimaging. Brain rhythms play a crucial role in diagnostics, cognitive research, and neurointerfaces. Here we demonstrate that a small number of OPMs can reliably capture sensorimotor rhythms (SMRs).<i>Approach.</i>We conducted movement execution and motor imagery (MI) experiments with nine participants in two distinct magnetically shielded rooms (MSRs), each equipped with different ambient field suppression systems. We used only 4 OPMs located above the sensorimotor region and standard common-spatial-patterns (CSPs) based processing to decode the real and imaginary movement intentions of our participants. We evaluated reproducibility of the CSP components' spectral profiles and assessed the decoding accuracy deterioration with reduction of OPM's count. We also assessed the influence of the magnetic field orientation on the decoding accuracy and implemented a real-time MI brain-computer interface (BCI) solution.<i>Main results.</i>Under optimal conditions, OPM sensors deliver informative signals suitable for practical MI BCI applications. Those subjects who participated in the experiments in both MSRs exhibit highly reproducible SMR spectral patterns across two different magnetically shielded environments. The magnetic field components with radial orientation yield higher decoding accuracy than their tangential counterparts. In some subjects we observed more than 80% of binary decoding accuracy using a single OPM sensor. Finally we demonstrate real-time performance of our system along with clearly pronounced and behaviorally relevant fluctuations of the SMR power.<i>Significance.</i>For the first time, we demonstrated reliable and reproducible tracking of SMR components using a small number of contactless OPM sensors during movement execution and MI. Our findings pave the way for more efficient post-stroke neurorehabilitation by enabling MI-based BCI solutions to accelerate functional recovery.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593272","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":"Characterizing the neural representations and decoding performance of foot rhythmic motor execution or imagery guided by action observation.","authors":"Xu Wang, Jianjun Meng, Yu Zheng, Yuxuan Wei, Fan Wang, Han Ding, Yan Zhuo","doi":"10.1088/1741-2552/adf011","DOIUrl":"10.1088/1741-2552/adf011","url":null,"abstract":"<p><p><i>Objective</i>. The limited spatial resolution inherent in electroencephalography (EEG), a widely-adopted non-invasive neuroimaging technique, combined with the intrinsic complexity of performing unilateral lower-limb motor imagery (MI), restricts decoding accuracy. To address these challenges, we propose a paradigm based on action observation-guided rhythmic motor execution (AO-ME) and motor imagery (AO-MI), designed to simplify task demands and enhance decoding performance. Magnetoencephalography (MEG) serves as the data acquisition method, leveraging its superior spatiotemporal resolution.<i>Approach</i>. Spatiotemporal and spectral features were characterized at the sensor level, and source imaging techniques were employed to examine cortical activation patterns. Ensemble task-related component analysis (eTRCA) facilitated decoding of unilateral tasks. And multiple decoding algorithms were employed to validate the effectiveness of the proposed paradigm.<i>Main results</i>. Robust lateralized neural responses were observed, exhibiting low-frequency phase-locked components that distinctly reflected the task frequency and its second harmonic within sensorimotor, parietal, and occipital cortices. Moreover, significant contralateral suppression of the sensorimotor rhythm was observed. Decoding accuracies reached 95.22 ± 4.75% for AO-ME and 88.66 ± 8.52% for AO-MI across twenty participants based on the phase-locked features using eTRCA.<i>Significance</i>. Collectively, our findings demonstrate that the proposed paradigm provides an effective approach for eliciting robust, distinguishable neural responses, enabling high decoding performance of unilateral lower-limb movements. This work offers new insights into the underexplored domain of lower-limb MI and highlights the paradigm's potential for brain-computer interface applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644474","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":"EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.","authors":"Yibing Li, Zhenye Zhao, Jiangchuan Liu, Yong Peng, Kenneth Camilleri, Wanzeng Kong, Andrzej Cichocki","doi":"10.1088/1741-2552/adeec8","DOIUrl":"10.1088/1741-2552/adeec8","url":null,"abstract":"<p><p><i>Objective.</i>Speech imagery is a nascent paradigm that is receiving widespread attention in current brain-computer interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.<i>Approach.</i>In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e. projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e. dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.<i>Main results.</i>To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.<i>Significance.</i>Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621620","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":"A primary mechanism for efficacy of the ketogenic diet may be energy repletion at the tripartite synapse.","authors":"Shubhada N Joshi, Aditya N Joshi, Narendra D Joshi","doi":"10.1088/1741-2552/adef7f","DOIUrl":"10.1088/1741-2552/adef7f","url":null,"abstract":"<p><p><i>Objective.</i>The ketogenic diet is a well-known treatment for epilepsy. Despite decades of research, it is not yet known how the diet accomplishes its anti-seizure efficacy. One of the earliest proposed mechanisms was that the ketogenic diet is able to replenish cellular energy stores in the brain. Although several mechanisms have been suggested for how energy depletion may contribute to seizure generation and epileptogenesis, how the dynamics of energy depletion actually leads to abnormal electrical activity is not known.<i>Approach.</i>In this work, we investigated the behavior of the tripartite synapse using a recently developed neurochemical model, which was modified to include ketone chemistry. We ran transient, non-steady-state simulations mimicking normoglycemia and ketosis for metabolic conditions known to be clinically treated with the ketogenic diet, as well as a condition for which the ketogenic diet was not effective clinically.<i>Main results.</i>We found that reduction in glucose, as well as pathological decreases in the activity of glucose transporter 1, pyruvate dehydrogenase complex, monocarboxylate transporter 1 (MCT1), and mitochondrial complex I, all led to functioning of the tripartite synapse in a rapid burst-firing mode suggestive of epileptiform activity. This was rescued by the addition of the ketone D-<i>β</i>-hydroxybutyrate in the glucose deficit, glucose transporter 1 deficiency, and pyruvate dehydrogenase complex deficiency, but not in MCT1 deficiency or mitochondrial complex I deficiency.<i>Significance.</i>We demonstrated that replenishment of cellular energy stores is a feasible mechanism for the efficacy of the ketogenic diet. Although we do not rule out other proposed mechanisms, our work suggests that cellular energy repletion may be the primary action of the ketogenic diet. Further study of the contribution of energy deficits to seizure onset and even epileptogenesis may yield novel therapies for epilepsy in the future.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639123","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}