{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T03:14:22Z","timestamp":1790738062272,"version":"4.1.0"},"reference-count":63,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["movementdisorders.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Movement Disorders"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting\u2010state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low\u2010frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD\u2010cognitive states, ranging from normal cognition (PD\u2010NC), mild cognitive impairment (PD\u2010MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Among 118 PD patients age\u2010, sex\u2010, and education matched with 35 healthy controls, 52 were classified with PD\u2010NC, 46 with PD\u2010MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs\u2010fMRI data and used to train ML models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>FD outperformed fALFF metrics in differentiating between PD\u2010cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study indicates that PD\u2010cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD\u2010cognitive decline. \u00a9 2023 The Authors. <jats:italic>Movement Disorders<\/jats:italic> published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mds.29678","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:57:19Z","timestamp":1701867439000},"page":"305-317","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7947-5903","authenticated-orcid":false,"given":"Eleonora","family":"Fiorenzato","sequence":"first","affiliation":[{"name":"Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN\u2010RND) 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