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However, the validation of specific brain network biomarkers in nonmotor symptom subtypes is currently lacking. By performing a new approach to compute functional connectivity with structural prior using magnetic resonance imaging, the present study computed both functional connectivity and fusional connectivity features in the nonmotor symptom subtypes of Parkinson\u2019s disease, one characterized by cognitive impairment with late onset and the other depression with early onset. The functional connectivity and fusional connectivity features centered at the left amygdala were both detected. The fusional features significantly enhanced the classification performance. The amygdala-postcentral and amygdala-orbital frontal features were critical for cognitive impairment with late onset detection, while the amygdala-temporooccipital features were crucial for depression with early onset detection. Additionally, the fusional connectivity features between the amygdala and the junction sulcus of parietooccipital and temporooccipital regions contributed significantly to differentiating cognitive impairment with late onset and depression with early onset. The within-subtype correlation analysis revealed that age at onset and cognitive scores were associated with features of amygdala-somatosensory\/visual-motor processing areas in cognitive impairment with late onset, while related to features of amygdala-emotional processing areas in depression with early onset. Our findings highlighted distinct amygdala-centered fusional connectivity features related to diverse nonmotor symptoms in Parkinson\u2019s disease, offering new insights for pathogenesis-targeted treatments for specific Parkinson\u2019s disease subtypes.<\/jats:p>","DOI":"10.1093\/cercor\/bhaf002","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T07:40:36Z","timestamp":1737531636000},"source":"Crossref","is-referenced-by-count":2,"title":["Amygdala-centered fusional connections characterized nonmotor symptoms in Parkinson\u2019s disease"],"prefix":"10.1093","volume":"35","author":[{"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Mathematical Sciences, University of Electronic Science and Technology of China , No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731 ,","place":["P. 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