Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

被引:5
|
作者
Fiorenzato, Eleonora [1 ]
Moaveninejad, Sadaf [2 ,3 ]
Weis, Luca [1 ,4 ,5 ]
Biundo, Roberta [1 ,6 ,7 ]
Antonini, Angelo [1 ,2 ,3 ,6 ]
Porcaro, Camillo [1 ,8 ,9 ,10 ]
机构
[1] Univ Padua, Ctr Rare Neurol Dis ERN RND, Dept Neurosci, Parkinsons Dis & Movement Disorders Unit, Padua, Italy
[2] Univ Padua, Dept Neurosci, Padua, Italy
[3] Univ Padua, Padova Neurosci Ctr, Padua, Italy
[4] Univ Birmingham, Ctr Human Brain Hlth, Birmingham, England
[5] Univ Birmingham, Sch Psychol, Birmingham, England
[6] San Camillo Hosp, IRCCS, Venice, Italy
[7] Univ Padua, Ctr Neurodegenerat Dis Res CESNE, Dept Neurosci, Padua, Italy
[8] Univ Padua, Dept Gen Psychol, Padua, Italy
[9] Inst Cognit Sci & Technol, Natl Res Council, Rome, Italy
[10] Univ Padua, Dept Neurosci, DNS, Via Giustiniani 5, I-35128 Padua, Italy
关键词
Parkinson's disease; fractal dimension; mild cognitive impairment; dementia; neural networks; FRACTAL DIMENSION; FUNCTIONAL CONNECTIVITY; CINGULATE CORTEX; IMPAIRMENT; DEMENTIA; CRITERIA;
D O I
10.1002/mds.29678
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
BackgroundHiguchi'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. (c) 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
引用
收藏
页码:305 / 317
页数:13
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