工程技术最新文献

{"title":"Metacognition of one's strategic planning in decision-making: the contribution of EEG correlates and individual differences.","authors":"Michela Balconi, Roberta A Allegretta, Laura Angioletti","doi":"10.1007/s11571-024-10189-8","DOIUrl":"10.1007/s11571-024-10189-8","url":null,"abstract":"<p><p>The metacognition of one's planning strategy constitutes a \"second-level\" of metacognition that goes beyond the knowledge and monitoring of one's cognition and refers to the ability to use awareness mechanisms to regulate execution of present or future actions effectively. This study investigated the relation between metacognition of one's planning strategy and the behavioral and electrophysiological (EEG) correlates that support strategic planning abilities during performance in a complex decision-making task. Moreover, a possible link between task execution, metacognition, and individual differences (i.e., personality profiles and decision-making styles) was explored. A modified version of the Tower of Hanoi task was proposed to a sample of healthy participants, while their behavioral and EEG neurofunctional correlates of strategic planning were collected throughout the task with decisional valence. After the task, a metacognitive scale, the 10-item Big Five Inventory, the General Decision-Making Style inventory, and the Maximization Scale were administered. Results showed that the metacognitive scale enables to differentiate between the specific dimensions and levels of metacognition that are related to strategic planning behavioral performance and decision. Higher EEG delta power over left frontal cortex (AF7) during task execution positively correlates with the metacognition of one's planning strategy for the whole sample. While increased beta activity over the left frontal cortex (AF7) during task execution, higher metacognitive beliefs of efficacy and less willingness to change their strategy a posteriori were correlated with specific personality profiles and decision-making styles. These findings allow researchers to delve deeper into the multiple facets of metacognition of one's planning strategy in decision-making.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"4"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanisms underlying EEG power changes during wakefulness in insomnia patients: a model-driven study.","authors":"Qiang Li, Hanxuan Wang, Rui Zhang","doi":"10.1007/s11571-024-10207-9","DOIUrl":"10.1007/s11571-024-10207-9","url":null,"abstract":"<p><p>Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding. In this paper, by combining the neural computational model with the real EEG data, we focus on exploring what's behind the EEG power changes for insomniac. We first develop a modified Liley model, named FSR-Liley, by respectively considering the fast and slow synaptic responses in inhibitory neurons along with the one-way projection between them. Then we introduce a parameter selection and evaluation method based on Markov chain Monte Carlo algorithm and Wasserstein distance, by which the sensitive parameters are selected automatically, and meanwhile, the optimal values of selected parameters are evaluated. Finally, through combining with EEG data, we determine the sensitive parameters in FSR-Liley and accordingly provide the mechanistic hypotheses: (1) decrease in <math><msub><mi>P</mi> <mrow><mi>e</mi> <msub><mi>i</mi> <mi>f</mi></msub> </mrow> </msub> </math> , corresponding to the input from the thalamus to cortical inhibitory population with fast synaptic response, leads to the increased theta and beta power; (2) decrease in <math><msub><mi>N</mi> <mrow><mi>e</mi> <msub><mi>i</mi> <mi>f</mi></msub> </mrow> </msub> </math> , corresponding to the projection from cortical excitatory population to inhibitory population with fast synaptic response, causes the increased gamma power. The results in this paper provide insights into the mechanisms of EEG power changes in insomnia and establish a theoretical foundation to support further experimental research.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"17"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11718038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational framework of neuronal-astrocytic network within the basal ganglia-thalamic circuits associated with Parkinson's disease.","authors":"Suyu Liu, Xiaohang Zhu, Weigang Sun","doi":"10.1007/s11571-025-10236-y","DOIUrl":"10.1007/s11571-025-10236-y","url":null,"abstract":"<p><p>Parkinson's disease is the neurodegenerative disorder which involves both neurons and non-neurons, and whose symptoms are usually represented by the error index and synchronization index in the computational study. This paper combines with the classical basal ganglia-thalamic network model and tripartite synapse model to explore the internal effects of astrocytes on the Parkinson's disease. The model simulates the firing patterns of the Parkinsonian state and healthy state, verifies the feasibility of the neural-glial model. The results show that the rate of production for IP <math><mmultiscripts><mrow></mrow> <mn>3</mn> <mrow></mrow></mmultiscripts> </math> modulate the frequency and amplitude of slow inward current for subthalamic nucleus, globus pallidus externa and interna in two modes. Increasing the rate of production for IP <math><mmultiscripts><mrow></mrow> <mn>3</mn> <mrow></mrow></mmultiscripts> </math> of subthalamic nucleus and globus pallidus externa can decrease the error index and presumably alleviate the Parkinson's disease. Increasing the rate of production for IP <math><mmultiscripts><mrow></mrow> <mn>3</mn> <mrow></mrow></mmultiscripts> </math> of globus pallidus externa and adjusting the rate of production for IP <math><mmultiscripts><mrow></mrow> <mn>3</mn> <mrow></mrow></mmultiscripts> </math> of subthalamic nucleus can result in the desynchronization of network in a regular way. These obtained results emphasize the effect of neurons (especially subthalamic nucleus and globus pallidus externa), astrocytes and their interaction on the Parkinson's disease. It enriches the evidence of involvement of astrocyte in Parkinson's disease, and proposes some cognitive points to the alleviation of Parkinson's disease.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"55"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Excitatory synaptic integration mechanism of three types of granule cells in the dentate gyrus.","authors":"Yue Mao, Ming Liu, Xiaojuan Sun","doi":"10.1007/s11571-025-10226-0","DOIUrl":"10.1007/s11571-025-10226-0","url":null,"abstract":"<p><p>Granule cells (GCs) are mainly responsible for receiving and integrating information from the entorhinal cortex and transferring it to the hippocampus to accomplish memory-related functions such as pattern separation. Owing to the heterogeneity of GCs, there are also two other subtypes, namely semilunar granule cells (SGCs) and hilar ectopic granule cells (HEGCs). In order to investigate their differences, here we examine the disparities in dendritic integration among the different subtypes of GCs. By utilizing biological experimental data, we developed detailed multi-compartment models for each type of GC. Our findings reveal that under the excitatory synaptic inputs (mediated by AMPA receptors), the dendritic integration of GCs, SGCs and HEGCs are linear, sublinear, and supralinear respectively. Furthermore, we propose that the sublinear integration observed in SGCs may be attributed to a high density of V-type potassium channels (K <math><mmultiscripts><mrow></mrow> <mtext>V</mtext> <mrow></mrow></mmultiscripts> </math> ) distributed in dendrites with smaller volume and higher input resistance; while the supralinear integration seen in HEGCs may be due to a high density of T-type calcium channels (Ca <math><mmultiscripts><mrow></mrow> <mtext>T</mtext> <mrow></mrow></mmultiscripts> </math> ) distributed in dendrites with larger volume and lower input resistance. Additionally, sodium channels, six types of potassium channels (K <math><mmultiscripts><mrow></mrow> <mtext>A</mtext> <mrow></mrow></mmultiscripts> </math> , K <math><mmultiscripts><mrow></mrow> <mtext>M</mtext> <mrow></mrow></mmultiscripts> </math> , sK <math><mmultiscripts><mrow></mrow> <mtext>DR</mtext> <mrow></mrow></mmultiscripts> </math> , fK <math><mmultiscripts><mrow></mrow> <mtext>DR</mtext> <mrow></mrow></mmultiscripts> </math> , BK, SK), and two types of calcium channels (Ca <math><mmultiscripts><mrow></mrow> <mtext>N</mtext> <mrow></mrow></mmultiscripts> </math> , Ca <math><mmultiscripts><mrow></mrow> <mtext>L</mtext> <mrow></mrow></mmultiscripts> </math> ) have minimal influence on their respective integration modes. We also found different integration modes exhibit varied somatic firing rates when subjected to different spatial synaptic activation sets, the HEGCs with the supralinear integration demonstrate higher somatic firing rates than the SGCs with the sublinear integration. These results provide theoretical insights into understanding the distinct roles played by these three subtypes of granule cells in memory-related functions within the dentate gyrus.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10226-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"40"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143406189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images.","authors":"Melahat Poyraz, Ahmet Kursad Poyraz, Yusuf Dogan, Selva Gunes, Hasan S Mir, Jose Kunnel Paul, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Filippo Molinari, Rajendra Acharya","doi":"10.1007/s11571-025-10235-z","DOIUrl":"10.1007/s11571-025-10235-z","url":null,"abstract":"<p><p>The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"53"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143691190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven early diagnosis of specific mental disorders: a comprehensive study.","authors":"Firuze Damla Eryılmaz Baran, Meric Cetin","doi":"10.1007/s11571-025-10253-x","DOIUrl":"https://doi.org/10.1007/s11571-025-10253-x","url":null,"abstract":"<p><p>One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including machine learning and deep learning models, can identify early signs of bipolar disorder, schizophrenia, autism spectrum disorder, depression, suicidality, and dementia by analyzing speech patterns, behaviors, and physiological data. These approaches increase diagnostic accuracy and enable timely intervention, which is crucial for effective treatment. This paper presents a comprehensive literature review of AI approaches applied to mental disorder detection using various data sources, such as survey, Electroencephalography (EEG) signal, text and image. Applications include predicting anxiety and depression levels in online games, detecting schizophrenia from EEG signals, detecting autism spectrum disorder, analyzing text-based indicators of suicidality and depression, and diagnosing dementia from magnetic resonance imaging images. eXtreme Gradient Boosting (XGBoost), light gradient-boosting machine (LightGBM), random forest (RF), support vector machine (SVM), K-nearest neighbor were designed as machine learning models, and convolutional neural networks (CNN), long short-term memory (LSTM) and gated recurrent unit (GRU) models suitable for the dataset were designed as deep learning models. Data preprocessing techniques such as wavelet transforms, normalization, clustering were used to optimize model performances, and hyperparameter optimization and feature extraction were performed. While the LightGBM technique had the highest performance with 96% accuracy for anxiety and depression prediction, the optimized SVM stood out with 97% accuracy. Autism spectrum disorder classification reached 98% accuracy with XGBoost, RF and LightGBM. The LSTM model achieved a high accuracy of 83% in schizophrenia diagnosis. The GRU model showed the best performance with 93% accuracy in text-based suicide and depression detection. In the detection of dementia, LSTM and GRU models have demonstrated their effectiveness in data analysis by reaching 99% accuracy. The findings of the study highlight the effectiveness of LSTM and GRU for sequential data analysis and their applicability in medical imaging or natural language processing. XGBoost and LightGBM are noted to be highly accurate ML tools for clinical diagnoses. In addition, hyperparameter optimization and advanced data pre-processing approaches are confirmed to significantly improve model performance. The results obtained with this study have revealed the potential to improve clinical decision support systems for mental disorders with AI, facilitating early diagnosis and personalized treatment strategies.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"70"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12052716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topographic differences in EEG microstates: distinguishing juvenile myoclonic epilepsy from frontal lobe epilepsy.","authors":"Ying Li, Lidao Xu, Yibo Zhao, Mingxian Meng, Yanan Chen, Bin Wang, Beijia Cui, Jin Liu, Jiuyan Han, Na Wang, Ting Zhao, Lei Sun, Zhe Ren, Xiong Han","doi":"10.1007/s11571-025-10255-9","DOIUrl":"https://doi.org/10.1007/s11571-025-10255-9","url":null,"abstract":"<p><p>This study aims to develop an exploratory classification model for Juvenile Myoclonic Epilepsy (JME) based on electroencephalogram (EEG) microstate features to assist clinical diagnosis and reduce misdiagnosis rates. A total of 123 participants were included in this study, consisting of 74 patients diagnosed with JME and 49 patients with Frontal Lobe Epilepsy (FLE). Resting-state EEG data were retrospectively collected from all participants. After preprocessing, microstate analysis was performed, and 24 microstate features (including duration, occurrence rate, coverage, and transition probability) were extracted and analyzed. Finally, the extracted microstate parameters were used to train six machine learning classifiers to distinguish between the two types of epilepsy. The performance of these models was assessed by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC). The study found that all parameters of microstate A showed high consistency between the two groups. However, the JME group exhibited lower occurrence and smaller coverage of microstate B compared to the FLE group, while showing longer durations for microstate C. Additionally, the transition probabilities from microstate B to C and D were lower in the JME group, while the transition probability from C to D was significantly higher. When EEG microstate features were integrated into the six machine learning classifiers, the linear discriminant analysis (LDA) algorithm achieved the best classification performance (accuracy of 76.4%, precision of 79.5%, and AUC of 0.817). This study found significant differences in EEG microstate characteristics between JME and FLE. Based on 24 microstate features, a classification model was successfully developed and validated. These findings underscore the potential of EEG microstates as neurophysiological biomarkers for distinguishing between these two epilepsy types.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10255-9.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"72"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rate of herbicide resistant weed development: A Canadian Prairie case study.","authors":"Chelsea Sutherland, Savannah Gleim, Simona Lubieniechi, Stuart J Smyth","doi":"10.1080/21645698.2025.2477231","DOIUrl":"10.1080/21645698.2025.2477231","url":null,"abstract":"<p><p>Genetically modified crop adoption in Canada has been the key driver in removing tillage as the lead form of weed control, due to increased weed control efficiency. Land use has transitioned from the use of summerfallow to continuous cropping, predominantly involving zero or minimum tillage practices. Prairie crop rotations have diversified away from mainly cereals to include three-year rotations of cereals, pulses, and oilseeds. Total herbicide volume applied has increased as crop production acres increased, but the rate of herbicide active ingredient applied per hectare has declined. Diverse crop rotations allow for weed control using herbicides with different modes of action, reducing selection pressure for resistant weed development. Herbicide-resistant weeds are an important concern for farmers, as the loss of key herbicides would make weed control exceedingly more difficult. The objective of this case study is to examine herbicide resistance weed development in the Canadian Prairies and to identify changes in resistance development following GM crop adoption.</p>","PeriodicalId":54282,"journal":{"name":"Gm Crops & Food-Biotechnology in Agriculture and the Food Chain","volume":"16 1","pages":"252-262"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11901363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alterations of synaptic plasticity and brain oscillation are associated with autophagy induced synaptic pruning during adolescence.","authors":"Hui Wang, Xiaxia Xu, Zhuo Yang, Tao Zhang","doi":"10.1007/s11571-024-10185-y","DOIUrl":"10.1007/s11571-024-10185-y","url":null,"abstract":"<p><p>Adolescent brain development is characterized by significant anatomical and physiological alterations, but little is known whether and how these alterations impact the neural network. Here we investigated the development of functional networks by measuring synaptic plasticity and neural synchrony of local filed potentials (LFPs), and further explored the underlying mechanisms. LFPs in the hippocampus were recorded in young (21 ~ 25 days), adolescent (1.5 months) and adult (3 months) rats. Long term potentiation (LTP) and neural synchrony were analyzed. The results showed that the LTP was the lowest in adolescent rats. During development, the theta coupling strength was increased progressively but there was no significant change of gamma coupling between young rats and adolescent rats. The density of dendrite spines was decreased progressively during development. The lowest levels of NR2A, NR2B and PSD95 were detected in adolescent rats. Importantly, it was found that the expression levels of autophagy markers were the highest during adolescent compared to that in other developmental stages. Moreover, there were more co-localization of autophagosome and PSD95 in adolescent rats. It suggests that autophagy is possibly involved in synaptic elimination during adolescence, and further impacts synaptic plasticity and neural synchrony.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"2"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beta-band oscillations and spike-local field potential synchronization in the motor cortex are correlated with movement deficits in an exercise-induced fatigue mouse model.","authors":"Xudong Zhao, Hualin Wang, Ke Li, Shanguang Chen, Lijuan Hou","doi":"10.1007/s11571-024-10182-1","DOIUrl":"10.1007/s11571-024-10182-1","url":null,"abstract":"<p><p>Fatigue, a complex and multifaceted symptom, profoundly influences quality of life, particularly among individuals suffering from chronic medical conditions or neurological disorders. This symptom not only exacerbates existing conditions but also hinders daily functioning, thereby perpetuating a vicious cycle of worsening symptoms and reduced physical activity. Given the pivotal role of the motor cortex (M1) in coordinating and executing voluntary movements, understanding how the cortex regulates fatigue is crucial. Despite its importance, the neural mechanisms underlying fatigue remain inadequately explored. In this study, we employed electrophysiological recordings in the M1 region of mice to investigate how excitation-inhibition dynamics and neural oscillations are regulated during exercise-induced fatigue. We observed that fatigue led to decreased voluntary physical activity and cognitive performance, manifesting as reduced running wheel distance, mean speed, exercise intensity, and exploratory behaviour. At the neural level, we detected increased firing frequencies for M1 neurons, including both pyramidal neurons and interneurons, along with heightened beta-band oscillatory activity and stronger coupling between beta-band oscillations and interneurons. These findings enhance our understanding of the mechanisms underlying fatigue, offering insights into behavioural, excitability, and oscillatory changes. The results of this study could pave the way for the development of novel intervention strategies to combat fatigue.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"3"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}