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This study aims to address these challenges by introducing a novel approach to enhance the detection and analysis of SCD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A Frequency Self\u2010Adaptive Wavelet Transform (FSAWT) model was developed and optimized for functional brain network (FBN) construction using resting\u2010state functional MRI (rs\u2010fMRI) data. The model dynamically selected \u201cgolden frequencies\u201d to improve the accuracy and interpretability of brain connectivity patterns. FBNs from 240 participants (106 SCD, 134 controls) were analyzed and compared using traditional methods, pearson correlation (PC) and sparse representation (SR). Receiver operating characteristic\u2010area under the curve (ROC\u2010AUC) analysis validated the classification results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our findings demonstrate that individuals with SCD exhibit distinct functional connectivity alterations, including reversed parahippocampal gyrus\u2010superior parietal gyrus connectivity\u2014suggesting early DMN disintegration, weakened temporoparietal pathways linked to memory deficits, and enhanced fusiform gyrus\u2010orbitofrontal connectivity. The frequency\u2010optimized SRWT method achieved superior diagnostic performance (83.71% accuracy, AUC = 0.84) with 82.11% sensitivity and 85.71% specificity, significantly outperforming traditional approaches (61.93% accuracy for PC), highlighting its potential for early SCD detection through these network\u2010based biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The FSAWT model offers a robust framework for early SCD detection by integrating frequency\u2010specific and cross\u2010frequency dynamics. While these findings highlight potential contributions to precision diagnostics and personalized interventions for neurodegenerative disorders, such applications remain to be established in future studies. Future applications may also explore multimodal neuroimaging and broader cognitive impairments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.71039","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T06:51:15Z","timestamp":1765176675000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Frequency\u2010Optimized Wavelet Networks for Early Detection of Subjective Cognitive Decline via Resting\u2010State fMRI"],"prefix":"10.1002","volume":"15","author":[{"given":"Xiereniguli","family":"Anayiti","sequence":"first","affiliation":[{"name":"Department of Medical Imaging Tongji Hospital School of Medicine Tongji University  Shanghai China"},{"name":"Institute of Medical Imaging Artificial Intelligence Tongji University School of Medicine  Shanghai 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