Affiliation:
1. Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN‐RND) University of Padova Padova Italy
2. Department of Neuroscience and Padova Neuroscience Center University of Padua Padua Italy
3. IRCCS San Camillo Hospital Venice Italy
4. Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE) University of Padova Padova Italy
5. Department of General Psychology University of Padua Padua Italy
6. Institute of Cognitive Sciences and Technologies‐National Research Council Rome Italy
7. Centre for Human Brain Health and School of Psychology University of Birmingham Birmingham United Kingdom
Abstract
AbstractBackgroundHiguchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting‐state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.ObjectivesThe aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low‐frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD‐cognitive states, ranging from normal cognition (PD‐NC), mild cognitive impairment (PD‐MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.MethodsAmong 118 PD patients age‐, sex‐, and education matched with 35 healthy controls, 52 were classified with PD‐NC, 46 with PD‐MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs‐fMRI data and used to train ML models.ResultsFD outperformed fALFF metrics in differentiating between PD‐cognitive 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.ConclusionsOur study indicates that PD‐cognitive 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‐cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Subject
Neurology (clinical),Neurology