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Resting-state fMRI data from VCIND patients and demographically matched normal controls were analyzed using voxel-wise FCS across six anatomical distance intervals to characterize short-, medium-, and long-range functional connectivity. Network-level analyses were also performed to identify large-scale network involvement. The results reveal a hierarchical distance-dependent organization of functional connectivity, with short-range connectivity concentrated in primary sensory systems and long-range connectivity increasingly involving higher-order association networks. In VCIND, significant alterations were observed particularly within the frontoparietal network, suggesting compensatory network reorganization associated with cognitive performance. These distance-dependent FCS alterations may serve as potential neuroimaging markers for early identification and risk stratification of VCIND and may also support longitudinal monitoring of functional network changes.<\/jats:p>","DOI":"10.1177\/15333175261491804","type":"journal-article","created":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T06:08:42Z","timestamp":1789798122000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Distance-dependent Functional Connectivity Strength Analysis Using Resting-State fMRI: Application to Vascular Cognitive Impairment, No Dementia"],"prefix":"10.1177","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3863-954X","authenticated-orcid":false,"given":"Qiong-Ge","family":"Li","sequence":"first","affiliation":[{"name":"Capital Medical University"},{"name":"Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Xing","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Shan","sequence":"additional","affiliation":[{"name":"Capital Medical University"},{"name":"Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Yan","family":"Yin","sequence":"additional","affiliation":[{"name":"Capital Medical University"},{"name":"Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Tang","sequence":"additional","affiliation":[{"name":"Capital Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Lu","sequence":"additional","affiliation":[{"name":"Capital Medical University"},{"name":"Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"e_1_3_8_2_2","first-page":"205","volume-title":"Handbook of clinical neurology","author":"Mansfield A","year":"2018","unstructured":"Mansfield A, Inness EL, McIlroy WE. 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