Depression symptomatology correlates with event-related potentials in Parkinson's disease: An affective priming study

Dissanayaka, N.; Au, Tiffany R.; Angwin, Anthony J.; Iyer, Kartik K.; O’Sullivan, John D.; Byrne, Gerard; Silburn, Peter A.; Marsh, Rodney; Mellick, George D.; Copland, David A.

doi:10.1016/j.jad.2018.11.094

Cited by 10 publications

(10 citation statements)

References 62 publications

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“…Our study has several limitations. First, our sample size was limited, although in line with prior EEG studies in PD patients with depression [28,29]. Second, all of our patients were medicated, and it is possible that medications could influence these EEG signals [30].…”

Section: Discussionmentioning

confidence: 99%

“…We report three main results. First, PD patients with depression had globally attenuated alpha (8-13 Hz) rhythms, as well as attenuated central beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma rhythms relative to PD patients without depression. Second, PD patients with depression had strong global differences in gamma rhythms relative to non-PD patients with depression.…”

Section: Introductionmentioning

confidence: 99%

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Resting-state EEG distinguishes depression in Parkinson’s disease

Espinoza

May

Anjum

et al. 2022

Preprint

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Introduction: Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is hard to diagnose and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. Methods: We recruited 18 PD patients, 18 PD patients with depression, and 12 demographically-similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine-learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD), to distinguish between groups. Results: We found differences between PD patients with and without depression in the alpha band (8-13 Hz) globally and in the beta (13-30 Hz) and gamma (30-80 Hz) bands in the central electrodes. From two minutes of resting-state EEG we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97% accuracy, and between PD patients with depression and non-PD patients with depression with 100% accuracy. We verified the robustness of our finding by confirming that the classification accuracy declines gracefully as data are truncated. Conclusions: We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiologically-based biomarkers for non-motor symptoms of PD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resting-state EEG distinguishes depression in Parkinson’s disease

Espinoza

May

Anjum

et al. 2022

Preprint

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“…Our task-state EEG data considered all prime-target word pairs of Event Related Potentials (ERP) data following baseline correction at −100 ms pre-prime stimulus to 0 ms, relative to the onset of the prime. Baseline corrected ERP windows from 0 to 1000 ms post-target stimulus were then used for synchrony analysis, as this captures the main event-related cortical activity observed with the task (Dissanayaka et al, 2017a; Dissanayaka et al, 2019). Only correct trial responses with reaction times within 2000 ms were included for final analysis.…”

Section: Methodsmentioning

confidence: 99%

Theta and gamma connectivity is linked with affective and cognitive symptoms in Parkinson’s disease

Iyer

Angwin

et al. 2020

Preprint

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Background The progression of Parkinsons disease (PD) can often exacerbate symptoms of depression, anxiety, and/or cognitive impairment. In this study, we explore the possibility that multiple brain network responses are associated with symptoms of depression, anxiety and cognitive impairment in PD. This association is likely to provide insights into a single multivariate relationship, where common affective symptoms occurring in PD cohorts are related with alterations to electrophysiological response. Methods 70 PD patients and 21 healthy age-matched controls (HC) participated in a high-density electroencephalography (EEG) study. Functional connectivity differences between PD and HC groups of oscillatory activity at rest and during completion of an emotion-cognition task were examined to identify key brain oscillatory activities. A canonical correlation analysis (CCA) was applied to identify a putative multivariate relationship between connectivity patterns and affective symptoms in PD groups. Results A CCA analysis identified a single mode of co-variation linking theta and gamma connectivity with affective symptoms in PD groups. Increases in frontotemporal gamma, frontal and parietal theta connectivity were related with increased anxiety and cognitive impairment. Decreases in temporal region theta and frontoparietal gamma connectivity were associated with higher depression ratings and PD patient age. Limitations This study only reports on optimal dosage of dopaminergic treatment (on state) in PD and did not investigate at off medication. Conclusions Theta and gamma connectivity during rest and task-states are linked to affective and cognitive symptoms within fronto-temporo-parietal networks, suggesting a potential assessment avenue for understanding brain-behavior associations in PD with electrophysiological task paradigms.

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“…Our task-state EEG data considered all prime-target word pairs of Event Related Potentials (ERP) data following baseline correction at -100 ms pre-prime stimulus to 0 ms, relative to the onset of the prime. Baseline corrected ERP windows from 0 to 1000 ms post-target stimulus were then used for synchrony analysis, as this captures the main event-related cortical activity observed with the task (Dissanayaka et al, 2017a;Dissanayaka et al, 2019).…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

“…Specifically, differences in fronto-temporo-parietal network response of oscillatory patterns will significantly be altered during resting-states and during completion of a cognitive task. To address our study aims, we measured individual changes to oscillatory brain activity in PD patients using high-density electroencephalography (HD-EEG) under two conditions: (i) resting-state; and (ii) during completion of a simple affective priming task (Dissanayaka et al, 2017a;Dissanayaka et al, 2019). Within these settings, our study also explores whether brain network changes found within PD groups at rest and during cognitive states, can be explained by a common multivariate association that ties together electrophysiological response with symptoms of anxiety, depression and cognitive impairment.…”

Section: Introductionmentioning

confidence: 99%