Evidence map›Paper›PMID 40969038›Full record

ArticleSensors (Basel, Switzerland)2025

A Novel PNDA-MMNet Model for Evaluating Dynamic Changes in the Brain State of Patients with PTSD During Neurofeedback Training.

Peng Ding, Lei Zhao, Anmin Gong, Wenya Nan, Yunfa Fu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Peng DingFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Lei ZhaoFaculty of Science, Kunming University of Science and Technology, Kunming 650500, China.ORCID 0000-0003-2775-0031
Anmin GongSchool of Information Engineering, Chinese People's Armed Police Force Engineering University, Xian 710086, China.
Wenya NanDepartment of Psychology, College of Education, Shanghai Normal University, Shanghai 200234, China.
Yunfa FuFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Funding

National Natural Science Foundation of China 82172058
6 · The paper itself

Abstract

backgroundMonitoring 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.

methodThis 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.

resultsThe proposed model achieves a brain state identification accuracy of 0.7428 ± 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.

conclusionThis research introduces a more effective approach for real-time monitoring and evaluation of PTSD patients' brain states during NFT, offering a quantitative method for assessing treatment efficacy and guiding therapeutic interventions.

Indexed as

BrainNeurofeedbackStress Disorders, Post-TraumaticAdultAlgorithmsElectroencephalographyFemaleHumansMalechanges in brain statemesoscopic networkneurofeedback trainingpost-traumatic stress disorderprocess noise dynamic adaptation

Identifiers

PMID40969038
PMCPMC12158300

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.