Evidence map›Paper›PMID 36203748›Full record

ArticleClinical parkinsonism & related disorders2022

A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease.

Arturo I Espinoza, Patrick May, Md Fahim Anjum, Arun Singh, Rachel C Cole, Nicholas Trapp, Soura Dasgupta, Nandakumar S Narayanan

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical parkinsonism & related disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06984757 (Mid-frontal Delta/Theta and Cognitive Control), which is not on this map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06984757 naenrolling by invitationnot on this map

Mid-frontal Delta/Theta and Cognitive Control

TypeinterventionalSponsorNandakumar NarayananRan2017 to 2029Enrolled635ConditionsParkinson&Amp, Amp, #39, s Disease (PD)ArmsDeep-brain Stimulation, Transcranial Magnetic Stimulation, Transcranial Alternating Current Stimulation
3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Arturo I EspinozaDepartment of Neurology, University of Iowa, United States.
Patrick MayDepartment of Electrical and Computer Engineering, University of Iowa, United States.
Md Fahim AnjumDepartment of Electrical and Computer Engineering, University of Iowa, United States.
Arun SinghDivision of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, United States.
Rachel C ColeDepartment of Neurology, University of Iowa, United States.
Nicholas TrappDepartment of Psychiatry, University of Iowa, United States.
Soura DasguptaDepartment of Electrical and Computer Engineering, University of Iowa, United States.
Nandakumar S NarayananDepartment of Neurology, University of Iowa, United States.

Funding

Mid-frontal delta/theta rhythms and cognitive control in PDR01NS100849 · NINDS · UNIVERSITY OF IOWA · PI JAMES F CAVANAGH, Nandakumar Narayanan · 2017 to 2026
$3.6M
NINDS NIH HHS R01 NS100849
6 · The paper itself

Abstract

Introduction: Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult 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. We conducted a pilot study of depression and resting-state EEG in PD. Methods: We recruited 18 PD patients without depression, 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 (30-50 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 gracefully declines as data are randomly truncated. Conclusions: Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. 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 neurophysiological markers for non-motor symptoms of PD.

Indexed as

ClassificationDepressionMachine learning algorithmParkinson’s diseaseScalp electroencephalography (EEG)

Identifiers

PMID36203748
PMCPMC9529981

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.