Journal of neural engineering最新文献

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Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding. 基于一维级联卷积自动编码器和自适应窗口阈值的癫痫发作自动检测。
Journal of neural engineering Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad883a
Sunday Timothy Aboyeji, Xin Wang, Yan Chen, Ijaz Ahmad, Lin Li, Zhenzhen Liu, Chen Yao, Guoru Zhao, Yu Zhang, Guanglin Li, Shixiong Chen
{"title":"Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding.","authors":"Sunday Timothy Aboyeji, Xin Wang, Yan Chen, Ijaz Ahmad, Lin Li, Zhenzhen Liu, Chen Yao, Guoru Zhao, Yu Zhang, Guanglin Li, Shixiong Chen","doi":"10.1088/1741-2552/ad883a","DOIUrl":"10.1088/1741-2552/ad883a","url":null,"abstract":"<p><p><i>Objective</i>. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.<i>Approach</i>. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.<i>Main results</i>. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h<sup>-1</sup>respectively for a typical patient in CHB-MIT datasets.<i>Significance</i>. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484398","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}
引用次数: 0
Changes in high-frequency neural inputs to muscles during movement cancellation. 运动取消时肌肉高频神经输入的变化。
Journal of neural engineering Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad8835
Blanka Zicher, Simon Avrillon, Jaime Ibáñez, Dario Farina
{"title":"Changes in high-frequency neural inputs to muscles during movement cancellation.","authors":"Blanka Zicher, Simon Avrillon, Jaime Ibáñez, Dario Farina","doi":"10.1088/1741-2552/ad8835","DOIUrl":"10.1088/1741-2552/ad8835","url":null,"abstract":"<p><p><i>Objective.</i>Cortical beta (13-30 Hz) and gamma (30-60 Hz) oscillations are prominent in the motor cortex and are known to be transmitted to the muscles despite their limited direct impact on force modulation. However, we currently lack fundamental knowledge about the saliency of these oscillations at spinal level. Here, we developed an experimental approach to examine the modulations in high-frequency inputs to motoneurons under different motor states while maintaining a stable force, thus constraining behaviour.<i>Approach.</i>Specifically, we acquired brain and muscle activity during a 'GO'/'NO-GO' task. In this experiment, the effector muscle for the task (tibialis anterior) was kept tonically active during the trials, while participants (<i>N</i>= 12) reacted to sequences of auditory stimuli by either keeping the contraction unaltered ('NO-GO' trials), or by quickly performing a ballistic contraction ('GO' trials). Motor unit (MU) firing activity was extracted from high-density surface and intramuscular electromyographic signals, and the changes in its spectral contents in the 'NO-GO' trials were analysed.<i>Main results.</i>We observed an increase in beta and low-gamma (30-45 Hz) activity after the 'NO-GO' cue in the MU population activity. These results were in line with the brain activity changes measured with electroencephalography. These increases in power occur without relevant alterations in force, as behaviour was restricted to a stable force contraction.<i>Significance.</i>We show that modulations in motor cortical beta and gamma rhythms are also present in muscles when subjects cancel a prepared ballistic action while holding a stable contraction in a 'GO'/'NO-GO' task. This occurs while force levels produced by the task effector muscle remain largely unaltered. Our results suggest that muscle recordings are informative also about motor states that are not force-control signals. This opens up new potential use cases of peripheral neural interfaces.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484309","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}
引用次数: 0
Neural decoding and feature selection methods for closed-loop control of avoidance behavior. 用于避让行为闭环控制的神经解码和特征选择方法。
Journal of neural engineering Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad8839
Jinhan Liu, Rebecca Younk, Lauren M Drahos, Sumedh S Nagrale, Shreya Yadav, Alik S Widge, Mahsa Shoaran
{"title":"Neural decoding and feature selection methods for closed-loop control of avoidance behavior.","authors":"Jinhan Liu, Rebecca Younk, Lauren M Drahos, Sumedh S Nagrale, Shreya Yadav, Alik S Widge, Mahsa Shoaran","doi":"10.1088/1741-2552/ad8839","DOIUrl":"10.1088/1741-2552/ad8839","url":null,"abstract":"<p><p><i>Objective.</i>Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.<i>Approach.</i>We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.<i>Main results.</i>Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.<i>Significance.</i>Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484314","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}
引用次数: 0
Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities. 基于经验概率的非稳态神经元钙离子尖峰列车稳健熵率估计
Journal of neural engineering Pub Date : 2024-10-28 DOI: 10.1088/1741-2552/ad6cf4
Sathish Ande, Srinivas Avasarala, Sarpras Swain, Ajith Karunarathne, Lopamudra Giri, Soumya Jana
{"title":"Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities.","authors":"Sathish Ande, Srinivas Avasarala, Sarpras Swain, Ajith Karunarathne, Lopamudra Giri, Soumya Jana","doi":"10.1088/1741-2552/ad6cf4","DOIUrl":"10.1088/1741-2552/ad6cf4","url":null,"abstract":"<p><p><i>Objective</i>. Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations.<i>Approach</i>. We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly.<i>Main results</i>. We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies.<i>Significance</i>. The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908727","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}
引用次数: 0
Deep learning-based spike sorting: a survey. 基于深度学习的尖峰排序:一项调查。
Journal of neural engineering Pub Date : 2024-10-25 DOI: 10.1088/1741-2552/ad8b6c
Luca Meyer, Majid Zamani, János Rokai, Andreas Demosthenous
{"title":"Deep learning-based spike sorting: a survey.","authors":"Luca Meyer, Majid Zamani, János Rokai, Andreas Demosthenous","doi":"10.1088/1741-2552/ad8b6c","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8b6c","url":null,"abstract":"<p><p><b>Objective.</b>Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.<b>Approach.</b>Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e., models that detect spikes and extract features or do classification within a single network, are included.<b>Main Results.</b>Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on ASICs and FPGAs. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.<b>Significance.</b>This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515454","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}
引用次数: 0
Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients. 研究双侧经颅磁刺激和虚拟现实脑机接口训练对慢性中风患者的协同神经调节作用
Journal of neural engineering Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad8836
Monica Afonso, Francisco Sánchez-Cuesta, Yeray González-Zamorano, Juan Pablo Romero, Athanasios Vourvopoulos
{"title":"Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients.","authors":"Monica Afonso, Francisco Sánchez-Cuesta, Yeray González-Zamorano, Juan Pablo Romero, Athanasios Vourvopoulos","doi":"10.1088/1741-2552/ad8836","DOIUrl":"10.1088/1741-2552/ad8836","url":null,"abstract":"<p><p><i>Objective.</i>Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.<i>Approach.</i>In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.<i>Main results.</i>Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.<i>Significance.</i>This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484313","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}
引用次数: 0
Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications. 利用扩散张量成像建立脑组织电阻抗模型,用于功能神经外科应用。
Journal of neural engineering Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad7db2
Niranjan Kumar, Aidan Ahamparam, Charles W Lu, Karlo A Malaga, Parag G Patil
{"title":"Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications.","authors":"Niranjan Kumar, Aidan Ahamparam, Charles W Lu, Karlo A Malaga, Parag G Patil","doi":"10.1088/1741-2552/ad7db2","DOIUrl":"10.1088/1741-2552/ad7db2","url":null,"abstract":"<p><p><i>Objective.</i>Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.<i>Approach.</i>We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.<i>Main results.</i>In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuch<i>et al</i>,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.<i>Significance.</i>This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locations<i>in vivo</i>. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304992","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}
引用次数: 0
TMS-induced phase resets depend on TMS intensity and EEG phase. TMS 诱导的相位复位取决于 TMS 强度和脑电图相位。
Journal of neural engineering Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad7f87
Brian Erickson, Brian Kim, Philip Sabes, Ryan Rich, Abigail Hatcher, Guadalupe Fernandez-Nuñez, Georgios Mentzelopoulos, Flavia Vitale, John Medaglia
{"title":"TMS-induced phase resets depend on TMS intensity and EEG phase.","authors":"Brian Erickson, Brian Kim, Philip Sabes, Ryan Rich, Abigail Hatcher, Guadalupe Fernandez-Nuñez, Georgios Mentzelopoulos, Flavia Vitale, John Medaglia","doi":"10.1088/1741-2552/ad7f87","DOIUrl":"10.1088/1741-2552/ad7f87","url":null,"abstract":"<p><p><i>Objective</i>. The phase of the electroencephalographic (EEG) signal predicts performance in motor, somatosensory, and cognitive functions. Studies suggest that brain phase resets align neural oscillations with external stimuli, or couple oscillations across frequency bands and brain regions. Transcranial Magnetic Stimulation (TMS) can cause phase resets noninvasively in the cortex, thus providing the potential to control phase-sensitive cognitive functions. However, the relationship between TMS parameters and phase resetting is not fully understood. This is especially true of TMS intensity, which may be crucial to enabling precise control over the amount of phase resetting that is induced. Additionally, TMS phase resetting may interact with the instantaneous phase of the brain. Understanding these relationships is crucial to the development of more powerful and controllable stimulation protocols.<i>Approach.</i>To test these relationships, we conducted a TMS-EEG study. We applied single-pulse TMS at varying degrees of stimulation intensity to the motor area in an open loop. Offline, we used an autoregressive algorithm to estimate the phase of the intrinsic<i>µ</i>-Alpha rhythm of the motor cortex at the moment each TMS pulse was delivered.<i>Main results</i>. We identified post-stimulation epochs where<i>µ</i>-Alpha phase resetting and N100 amplitude depend parametrically on TMS intensity and are significant<i>versus</i>peripheral auditory sham stimulation. We observed<i>µ</i>-Alpha phase inversion after stimulations near peaks but not troughs in the endogenous<i>µ</i>-Alpha rhythm.<i>Significance</i>. These data suggest that low-intensity TMS primarily resets existing oscillations, while at higher intensities TMS may activate previously silent neurons, but only when endogenous oscillations are near the peak phase. These data can guide future studies that seek to induce phase resetting, and point to a way to manipulate the phase resetting effect of TMS by varying only the timing of the pulse with respect to ongoing brain activity.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11500019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142335339","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}
引用次数: 0
An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity. 在中风后手指灵活性恢复过程中,大脑皮层与皮层下部之间的相互作用是一个 ANN 模型。
Journal of neural engineering Pub Date : 2024-10-21 DOI: 10.1088/1741-2552/ad8961
Ashraf Kadry, Deborah Solomonow-Avnon, Sumner Lee Norman, Jing Xu, Firas Mawase
{"title":"An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity.","authors":"Ashraf Kadry, Deborah Solomonow-Avnon, Sumner Lee Norman, Jing Xu, Firas Mawase","doi":"10.1088/1741-2552/ad8961","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8961","url":null,"abstract":"<p><strong>Objective: </strong>Finger dexterity, and finger individuation in particular, is crucial for human movement, and disruptions due to brain injury can significantly impact quality of life. Understanding the neurological mechanisms responsible for recovery is vital for effective neurorehabilitation. This study explores the role of two key pathways in finger individuation: the corticospinal tract (CST) from the primary motor cortex and premotor areas, and the subcortical reticulospinal tract (RST) from the brainstem. We aimed to investigate how the cortical-reticular network reorganizes to aid recovery of finger dexterity following lesions in these areas.</p><p><strong>Approach: </strong>To provide a potential biologically plausible answer to this question, we developed an artificial neural network (ANN) to model the interaction between a premotor planning layer, a cortical layer with excitatory and inhibitory corticospinal outputs, and reticulospinal outputs controlling finger movements. The ANN was trained to simulate normal finger individuation and strength. A simulated stroke was then applied to the corticospinal (CS) area, reticulospinal (RS) area, or both, and the recovery of finger dexterity was analyzed.</p><p><strong>Main results: </strong>In the intact model, the ANN demonstrated a near-linear relationship between the forces of instructed and uninstructed fingers, resembling human individuation patterns. Post-stroke simulations revealed that lesions in both CS and RS regions led to increased unintended force in uninstructed fingers, immediate weakening of instructed fingers, improved control during early recovery, and increased neural plasticity. Lesions in the CS region alone significantly impaired individuation, while RS lesions affected strength and to a lesser extent, individuation. The model also predicted the impact of stroke severity on finger individuation, highlighting the combined effects of CS and RS lesions.</p><p><strong>Significance: </strong>This model provides insights into the interactive role of cortical and subcortical regions in finger individuation. It suggests that recovery mechanisms involve reorganization of these networks, which may inform neurorehabilitation strategies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484397","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}
引用次数: 0
Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings. 全局运动动力学--分布式全脑记录中运动行为的不变神经表征。
Journal of neural engineering Pub Date : 2024-10-21 DOI: 10.1088/1741-2552/ad851c
Maarten C Ottenhoff, Maxime Verwoert, Sophocles Goulis, Louis Wagner, Johannes P van Dijk, Pieter L Kubben, Christian Herff
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