{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:31:47Z","timestamp":1772137907486,"version":"3.50.1"},"reference-count":31,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001411","name":"Indian Council of Medical Research","doi-asserted-by":"crossref","award":["ISRM\/12(54)\/2022"],"award-info":[{"award-number":["ISRM\/12(54)\/2022"]}],"id":[{"id":"10.13039\/501100001411","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Cognitive Science Research Initiative, DST, Govt of India","award":["DST\/CSRI\/2018\/11"],"award-info":[{"award-number":["DST\/CSRI\/2018\/11"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Background<\/jats:italic>\n                    . Intracranial dural arteriovenous fistula (DAVF) is an acquired vascular condition involving abnormal connections between dural arteries and veins without intervening capillary beds. Cognitive impairment is a common symptom in DAVFs, often linked to disrupted brain network connectivity. Resting-state functional MRI (rsfMRI) allows for examining functional connectivity through blood oxygenation level dependent (BOLD) signal analysis. However, rsfMRI signals exhibit fractal behavior that complicates connectivity analysis. This study explores nonfractal connectivity as a potential biomarker for cognitive impairment in DAVF patients by isolating short-memory components in BOLD signals.\n                    <jats:italic>Method<\/jats:italic>\n                    . 50 DAVF patients and 50 healthy controls underwent neuropsychological assessments and rsfMRI. Preprocessed BOLD signals were decomposed using wavelet transforms to isolate fractal and nonfractal components. Connectivity matrices based on fractal, nonfractal, and Pearson correlation components were generated and used as features for classification. Machine learning classifiers, including SVM and decision trees, were optimized via cross-validation in MATLAB, with performance assessed by accuracy, sensitivity, specificity, and AUC.\n                    <jats:italic>Results<\/jats:italic>\n                    . Nonfractal connectivity outperformed fractal and Pearson correlation measures, achieving a classification accuracy of 89.82% using SVM, with high sensitivity (86.54%), specificity (92.4%), and an AUC of 0.96. Nonfractal connectivity effectively differentiated cognitive impairment in DAVFs, offering a clearer depiction of neural activity by reducing the influence of fractal patterns.\n                    <jats:italic>Conclusion<\/jats:italic>\n                    . This study suggests that nonfractal connectivity is a promising biomarker for assessing cognitive impairment in DAVF patients, potentially supporting early diagnosis and intervention. While nonfractal analysis showed promising classification accuracy, further research with larger datasets is needed to validate these findings and explore applicability in other neurological conditions.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/adf3bb","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:47:54Z","timestamp":1753397274000},"page":"055009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fractal analysis for cognitive impairment classification in DAVF using machine learning"],"prefix":"10.1088","volume":"11","author":[{"given":"Jithin","family":"Sivan Sulaja","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santhosh Kumar","family":"Kannath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramshekhar N","family":"Menon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0355-3375","authenticated-orcid":true,"given":"Bejoy","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"bpexadf3bbbib1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.7461\/jcen.2022.E2021.04.001","article-title":"Pathophysiology and classification of intracranial and spinal dural AVF","volume":"24","author":"Sim","year":"2022","journal-title":"J. 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