Computer methods and programs in biomedicine · 2026
A Parameter-free unsupervised framework for fMRI data analysis using batch learning growing neural gas and spatial-temporal false positive control.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Khani Tania Hossein, Tajarrod Amir Hossein, Shamsi Mousa, Zarei Asghar
Аннотация
BACKGROUND AND OBJECTIVE: Clustering methods are essential for analyzing functional magnetic resonance imaging (fMRI) time-series data to identify active brain regions and elucidate functional neural patterns. Despite recent advancements in enhancing the stability and automation of cluster number selection, many methods still rely on user-defined parameters, which complicates their application.
METHODS: To deal with these issues, this study introduces a novel parameter-free hierarchical topological structure learning clustering algorithm, Batch Learning Growing Neural Gas (BL-GNG), which builds on the Growing Neural Gas (GNG) model to improve convergence speed and eliminate the need for manual parameter tuning. Then, a two-stage false positive rate control mechanism, based on randomization inference and three-dimensional neighborhood criteria, further enhances the algorithm's robustness is proffered.
RESULTS: The performance of BL-GNG was evaluated on real fMRI data targeting auditory cortex activity, using the General Linear Model in SPM with a Family-Wise Error-corrected threshold of p < 0.05 as the ground truth. Compared to K-means, Fuzzy C-means (FCM), Neural Gas (NG), and GNG. BL-GNG achieved a Jaccard Coefficient of 0.99 and an Area Under the ROC Curve of 0.97, demonstrating superior exactitude and stability across 50 iterative runs. With an average execution time of 26 s, the algorithm also offers significant computational efficiency.
CONCLUSIONS: These results highlight BL-GNG's potential as a powerful tool for fMRI analysis, with applications in diagnosing brain disorders, investigating neural subnetworks, and advancing cognitive neuroscience research.
METHODS: To deal with these issues, this study introduces a novel parameter-free hierarchical topological structure learning clustering algorithm, Batch Learning Growing Neural Gas (BL-GNG), which builds on the Growing Neural Gas (GNG) model to improve convergence speed and eliminate the need for manual parameter tuning. Then, a two-stage false positive rate control mechanism, based on randomization inference and three-dimensional neighborhood criteria, further enhances the algorithm's robustness is proffered.
RESULTS: The performance of BL-GNG was evaluated on real fMRI data targeting auditory cortex activity, using the General Linear Model in SPM with a Family-Wise Error-corrected threshold of p < 0.05 as the ground truth. Compared to K-means, Fuzzy C-means (FCM), Neural Gas (NG), and GNG. BL-GNG achieved a Jaccard Coefficient of 0.99 and an Area Under the ROC Curve of 0.97, demonstrating superior exactitude and stability across 50 iterative runs. With an average execution time of 26 s, the algorithm also offers significant computational efficiency.
CONCLUSIONS: These results highlight BL-GNG's potential as a powerful tool for fMRI analysis, with applications in diagnosing brain disorders, investigating neural subnetworks, and advancing cognitive neuroscience research.