{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:13:52Z","timestamp":1784301232356,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82172058"],"award-info":[{"award-number":["82172058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Monitoring and evaluating dynamic changes in brain states during electroencephalography (EEG) neurofeedback training (NFT) for post-traumatic stress disorder (PTSD) patients remains challenging when using traditional methods. Method: This study proposes a novel Process Noise Dynamic Adaptation-Mesoscale Mesonetwork Network (PNDA-MMNet) model, which improves upon conventional techniques by establishing a discrete linear dynamic model of the NFT process. The model utilizes a mesoscale intermediate network architecture to create a brain state observation matrix, computes the brain state transition matrix, and applies fuzzy rules for dynamic adaptive noise processing. This maximizes the separability between brain state transitions during NFT and resting states. Results: The proposed model achieves a brain state identification accuracy of 0.7428 \u00b1 0.12 (area under the curve, AUC = 0.84), significantly outperforming conventional algorithms. Interpretations of the model indicate that continuous NFT reduces functional connectivity within the motor cortex, with stronger suppression in the right hemisphere compared to the left. Additionally, it reveals decreased activity in the occipital cortex, particularly in the left occipital region, where inhibition increases radially from the midline. Notably, the connectivity between the motor and occipital cortices remains stable throughout the training process. These connectivity changes reflect NFT-induced modulation of cortical activity and are consistent with known neurophysiological patterns in PTSD, highlighting their potential relevance to therapeutic mechanisms. Conclusion: This research introduces a more effective approach for real-time monitoring and evaluation of PTSD patients\u2019 brain states during NFT, offering a quantitative method for assessing treatment efficacy and guiding therapeutic interventions.<\/jats:p>","DOI":"10.3390\/s25113522","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T06:21:51Z","timestamp":1748931711000},"page":"3522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel PNDA-MMNet Model for Evaluating Dynamic Changes in the Brain State of Patients with PTSD During Neurofeedback Training"],"prefix":"10.3390","volume":"25","author":[{"given":"Peng","family":"Ding","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2775-0031","authenticated-orcid":false,"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anmin","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chinese People\u2019s Armed Police Force Engineering University, Xian 710086, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenya","family":"Nan","sequence":"additional","affiliation":[{"name":"Department of Psychology, College of Education, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfa","family":"Fu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/jts.20104","article-title":"Ten Years After the Genocide: Trauma Confrontation and Post Traumatic Stress in Rwandan Adolescents","volume":"19","author":"Schaal","year":"2006","journal-title":"J. Trauma. Stress"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1001\/jama.292.5.602","article-title":"Trauma and PTSD symptoms in Rwanda: Implications for attitudes toward justice and reconciliation","volume":"292","author":"Pham","year":"2004","journal-title":"JAMA"},{"key":"ref_3","unstructured":"Bazivamo, C. (2002, November 30). D\u00e9nombrement des Victimes du G\u00e9nocide, Rapport Final, Version R\u00e9vis\u00e9e. Available online: https:\/\/francegenocidetutsi.org\/DenombrementDesVictimesDuGenocidePerpetreContreLesTutsiAvril2004.html.en."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jad.2020.06.017","article-title":"Burden of Post-Traumatic Stress Disorder in Postgenocide Rwandan Population Following Exposure to 1994 Genocide Against the Tutsi: A Meta-Analysis","volume":"275","author":"Musanabaganwa","year":"2020","journal-title":"J. Affect. Disord."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1016\/j.jad.2021.08.071","article-title":"Neurofeedback with Low-Cost, Wearable Electroencephalography (EEG) Reduces Symptoms in Chronic Post-Traumatic Stress Disorder","volume":"295","author":"Bigirimana","year":"2021","journal-title":"J. Affect. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27313","DOI":"10.3402\/ejpt.v6.27313","article-title":"Restoring Large-Scale Brain Networks in PTSD and Related Disorders: A Proposal for Neuroscientifically-Informed Treatment Interventions","volume":"6","author":"Lanius","year":"2015","journal-title":"Eur. J. Psychotraumatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108066","DOI":"10.1016\/j.dib.2022.108066","article-title":"Electroencephalography and Psychological Assessment Datasets to Determine the Efficacy of a Low-Cost, Wearable Neurotechnology Intervention for Reducing Post-Traumatic Stress Disorder Symptom Severity","volume":"42","author":"Bigirimana","year":"2022","journal-title":"Data Brief"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"fcaa049","DOI":"10.1093\/braincomms\/fcaa049","article-title":"Activity or Connectivity? A Randomized Controlled Feasibility Study Evaluating Neurofeedback Training in Huntington\u2019s Disease","volume":"2","author":"Papoutsi","year":"2020","journal-title":"Brain Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"67","DOI":"10.9758\/cpn.23.1054","article-title":"The Effect of Mobile Neurofeedback Training in Children with Attention Deficit Hyperactivity Disorder: A Randomized Controlled Trial","volume":"22","author":"Kwon","year":"2023","journal-title":"Clin. Psychopharmacol. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2256206","DOI":"10.1080\/20008066.2023.2256206","article-title":"Integration of Limbic Self-Neuromodulation with Psychotherapy for Complex Post-Traumatic Stress Disorder: Treatment Rationale and Case Study","volume":"15","author":"Fine","year":"2024","journal-title":"Eur. J. Psychotraumatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112301","DOI":"10.1016\/j.ijpsycho.2024.112301","article-title":"Down-Regulation of Theta Amplitude Through Neurofeedback Improves Executive Control Network Efficiency in Healthy Children","volume":"197","author":"Xiong","year":"2024","journal-title":"Int. J. Psychophysiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e101091","DOI":"10.1136\/gpsych-2023-101091","article-title":"Resting-State Electroencephalography Theta Predicts Neurofeedback Treatment 4-Month Follow-Up Response in Nicotine Addiction","volume":"36","author":"Meng","year":"2023","journal-title":"Gen. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/TAFFC.2021.3134183","article-title":"Neurofeedback Training with an Electroencephalogram-Based Brain\u2013Computer Interface Enhances Emotion Regulation","volume":"14","author":"Huang","year":"2023","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10484-011-9165-y","article-title":"Neurofeedback for Insomnia: A Pilot Study of Z-Score SMR and Individualized Protocols","volume":"36","author":"Hammer","year":"2011","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"191","DOI":"10.51668\/bp.8321110n","article-title":"Effects of Quantitative Electroencephalogram Normalization Using 4-Channel Live Z-Score Training Neurofeedback for Children with Learning Disabilities: Preliminary Data","volume":"29","author":"Carrobles","year":"2021","journal-title":"Behav. Psychol. Conduct."},{"key":"ref_16","unstructured":"G.Tec Medical Engineering (2020, May 24). G.Nautilus Research|Wearable EEG Headset. Available online: https:\/\/www.gtec.at\/product\/gnautilus-research\/."},{"key":"ref_17","unstructured":"NeuroCONCISE Ltd (2021, September 08). NeuroCONCISE. Available online: https:\/\/www.neuroconcise.co.uk\/."},{"key":"ref_18","unstructured":"Schruben, T.L.W. (2000, January 10\u201313). Mathematical Programming Models of Discrete Event System Dynamics. Proceedings of the WSC 2000, Winter Simulation Conference, Orlando, FL, USA."},{"key":"ref_19","unstructured":"Logothetis, A., and Krishnamurthy, V. (2002, January 12). MAP State Sequence Estimation for Jump Markov Linear Systems via the Expectation-Maximization Algorithm. Proceedings of the 36th IEEE Conference on Decision and Control, San Diego, CA, USA."},{"key":"ref_20","first-page":"24","article-title":"Extension of Kalman Filtering When Noise Could Be Non-Additive","volume":"44","author":"Wang","year":"2008","journal-title":"Comput. Eng. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, L., Sun, X., He, H., and Zhang, Y. (2020, January 19\u201321). An Analytical Discretization Approach to Continuous-Time System for Kalman Filter. Proceedings of the 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), Harbin, China.","DOI":"10.1109\/DDCLS49620.2020.9275088"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/TAC.2013.2264739","article-title":"Kalman Filter for Discrete-Time Stochastic Linear Systems Subject to Intermittent Unknown Inputs","volume":"58","author":"Keller","year":"2013","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"899","DOI":"10.21629\/JSEE.2018.05.02","article-title":"Properties of Gauss-Newton Filter in Linear Cases","volume":"29","author":"Zhichao","year":"2018","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sobolic, F.M., and Bernstein, D.S. (2016, January 6\u20138). Kalman-Filter-Based Time-Varying Parameter Estimation via Retrospective Optimization of the Process Noise Covariance. Proceedings of the 2016 American Control Conference (ACC), Boston, MA, USA.","DOI":"10.1109\/ACC.2016.7526068"},{"key":"ref_25","unstructured":"Llewellyn, M.E., Barretto, R.J., Delp, S.L., and Schnitzer, M.J. (2009). Method and System of Using Intrinsic-Based Photosensing with High-Speed Line Scanning for Characterization of Biological Thick Tissue Including Muscle. (Application No. 12\/165,977), U.S. Patent."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kim, H., Luo, J., Chu, S., Cannard, C., Hoffmann, S., and Miyakoshi, M. (2023). ICA\u2019s bug: How ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing. Front. Signal Process., 3.","DOI":"10.3389\/frsip.2023.1064138"},{"key":"ref_27","first-page":"2","article-title":"Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization","volume":"123","author":"Jiao","year":"2020","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yanar, H., and Mishchenko, Y. (2016, January 16\u201319). A Hidden Markov Model of Electroencephalographic Brain Activity for Advanced EEG-Based Brain\u2013Computer Interfaces. Proceedings of the 2016 Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey.","DOI":"10.1109\/SIU.2016.7495747"},{"key":"ref_29","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Laurens","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dutta, T.K., Nayak, D.R., and Zhang, Y.-D. (2024). ARM-Net: Attention-Guided Residual Multiscale CNN for Multiclass Brain Tumor Classification Using MR Images. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105421"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"14604582221140975","DOI":"10.1177\/14604582221140975","article-title":"Ensemble Learning-Based Functional Independence Ability Estimator for Pediatric Brain Tumor Survivors","volume":"28","author":"Lin","year":"2022","journal-title":"Health Inform. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3109\/0954898X.2014.979900","article-title":"Latching Chains in K-Nearest-Neighbor and Modular Small-World Networks","volume":"26","author":"Song","year":"2015","journal-title":"Netw. Comput. Neural Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tang, C., Yang, L., Cao, G., Du, J., and Zhang, Q. (2024, January 3\u20136). ST-GCN: EEG Emotion Recognition via Spectral Graph and Temporal Analysis with Graph Convolutional Networks. Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisboa, Portugal.","DOI":"10.1109\/BIBM62325.2024.10822712"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TAFFC.2023.3318321","article-title":"AMDET: Attention Based Multiple Dimensions EEG Transformer for Emotion Recognition","volume":"15","author":"Xu","year":"2024","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/TPDS.2024.3522781","article-title":"HpT: Hybrid Acceleration of Spatio-Temporal Attention Model Training on Heterogeneous Manycore Architectures","volume":"36","author":"Dahal","year":"2025","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.biopsych.2006.06.004","article-title":"Neurocircuitry Models of Posttraumatic Stress Disorder and Extinction: Human Neuroimaging Research\u2014Past, Present, and Future","volume":"60","author":"Rauch","year":"2006","journal-title":"Biol. Psychiatry"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"517","DOI":"10.31887\/DCNS.2010.12.4\/raupperle","article-title":"Neural Systems Underlying Approach and Avoidance in Anxiety Disorders","volume":"12","author":"Aupperle","year":"2010","journal-title":"Dialogues Clin. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/11\/3522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:46:32Z","timestamp":1760031992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/11\/3522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":37,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["s25113522"],"URL":"https:\/\/doi.org\/10.3390\/s25113522","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,3]]}}}