{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:51:01Z","timestamp":1790103061960,"version":"4.0.1"},"reference-count":51,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2023,8,15]]},"abstract":"<jats:p>Background: Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types. Objective: This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment\/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network. Methods: A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer\u2019s disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network. Results: Our findings revealed a monotonic trend of disruption in the brain WM network (VaD\u200a&gt;\u200aMixD\u200a&gt;\u200aAD\u200a&gt;\u200aMCI\u200a&gt;\u200aSCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately. Conclusion: These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment\/dementia, and these measurements can provide valuable cognition-related information.<\/jats:p>","DOI":"10.3233\/jad-230341","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T12:08:27Z","timestamp":1688472507000},"page":"1487-1502","source":"Crossref","is-referenced-by-count":10,"title":["Disrupted Structural White Matter Network in Alzheimer\u2019s Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study"],"prefix":"10.1177","volume":"94","author":[{"given":"Hui-Qin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"},{"name":"Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anson C.M.","family":"Chau","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China"},{"name":"Medical Radiation Science, Allied Health and Human Performance, University of South Australia, Adelaide, Australia"},{"name":"Alliance for Research in Exercise, Nutrition, and Activity (ARENA), University of South Australia, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yat-Fung","family":"Shea","sequence":"additional","affiliation":[{"name":"Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick Ka-Chun","family":"Chiu","sequence":"additional","affiliation":[{"name":"Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Wen","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China"},{"name":"Department of Medical Imaging Center, The Affiliated Huaian No.1 People\u2019s Hospital, Nanjing Medical University, Huai\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry Ka-Fung","family":"Mak","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China"},{"name":"Alzheimer\u2019s Disease Research Network, The University of Hong Kong, Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-230341_ref1","unstructured":"Gauthier S , Rosa-Neto P , Morais JA , Webster C (2021) World Alzheimer Report 2021. 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