Abstract
ObjectiveCognitive impairment occurs frequently in Parkinson’s disease (PD) and negatively impacts the patient’s quality of life. However, its pathophysiological mechanism remains unclear, hindering the development of new therapies. Changes in brain connectivity are related to cognitive impairment in patients with PD, with the dorsolateral prefrontal cortex (DLPFC) being considered the essential region related to PD cognitive impairment. Nevertheless, few studies have focused on the global connectivity responsible for communication with the DLPFC node, the posterior division of the middle frontal gyrus (PMFG) in patients with PD; this was the focus of this study.MethodsWe applied resting-state electroencephalography (EEG) and calculated a reliable functional connectivity measurement, the debiased weighted phase lag index (dWPLI), to examine inter-regional functional connectivity in 68 patients with PD who were classified into two groups according to their cognitive condition.ResultsWe observed that altered left and right PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed condition (r = −0.426, p < 0.001 and r = −0.437, p < 0.001, respectively). Exploratory results based on the MoCA subdomains indicated that poorer visuospatial function was associated with higher right PMFG-based functional connectivity (r = −0.335, p = 0.005), and poorer attention function was associated with higher left and right PMFG-based functional connectivity (r = −0.380, p = 0.001 and r = −0.256, p = 0.035, respectively). Further analysis using logistic regression and receiver operating characteristic (ROC) curves found that this abnormal functional connectivity was an independent risk factor for cognitive impairment [odds ratio (OR): 2.949, 95% confidence interval (CI): 1.294–6.725, p = 0.01 for left PMFG; OR: 11.278, 95% CI: 2.578–49.335, p = 0.001 for right PMFG, per 0.1 U], and provided moderate classification power to discriminate between cognitive abilities in patients with PD [area under the ROC curve (AUC) = 0.770 for left PMFG; AUC = 0.809 for right PMFG].ConclusionThese preliminary findings indicate that abnormal PMFG-based functional connectivity patterns associated with cognitive impairment in the theta frequency bands under the eyes closed condition and altered functional connectivity patterns have the potential to act as reliable biomarkers for identifying cognitive impairment in patients with PD.
Highlights
Parkinson’s disease (PD) is clinically characterized by the presence of motor symptoms, including bradykinesia, tremor, rigidity, and postural instability (Sveinbjornsdottir, 2016)
Several EEG studies have found brain connectivity changes associated with cognitive impairment in patients with PD (Bertrand et al, 2016; Hassan et al, 2017; Chaturvedi et al, 2019; Sanchez-Dinorin et al, 2021), which might present potential markers for cognitive dysfunction, various alterations in different frequency bands were observed in these studies
Using resting-state EEG dWPLI measurement, the present study revealed abnormal PMFG-based functional connectivity associated with cognitive impairment in patients with PD in the theta frequency bands under the eyes closed (EC) condition
Summary
Parkinson’s disease (PD) is clinically characterized by the presence of motor symptoms, including bradykinesia, tremor, rigidity, and postural instability (Sveinbjornsdottir, 2016). Due to this linear mixing of signals from different brain regions detected by the same sensor, common methods used for functional connectivity evaluation (coherence or mutual information) may lead to the identification of transparent functional connections that do not accurately reflect the interactions between brain regions (Vinck et al, 2011). The weighted phase lag index (wPLI), which weighs the contribution of the observed phase lead or lags by the magnitude of the imaginary component of the cross-spectrum, is less sensitive to additive volume-conducted noise sources (Lau et al, 2012) Vinck et al proposed a debiased estimator of the squared WPLI [i.e., dWPLI (Vinck et al, 2011)] that has been frequently applied to assess EEG data (Hardmeier et al, 2014; Wang et al, 2017)
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