Abstract
To assess the cortical activity in people with Parkinson’s disease (PwP) with different motor phenotype (tremor-dominant—TD and postural instability and gait difficulty—PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.
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Abbreviations
- ADL:
-
Activities of daily living
- BMI:
-
Body mass index
- CG:
-
Control group
- CLH:
-
Contralateral hemisphere
- DAR:
-
Delta-alpha ratio
- EEG:
-
Electroencephalography
- EHS:
-
Edinburgh Handedness scale
- FFT:
-
Fast Fourier transform
- fMRI:
-
Functional magnetic resonance image
- GDS:
-
Geriatric depression scale
- H&Y:
-
Hoehn & Yahr modified scale
- LED:
-
Levodopa equivalent dosage
- MoCA:
-
Montreal cognitive assessment
- NA:
-
Not applied
- PD:
-
Parkinson’s disease
- PDSS:
-
Parkinson’s disease sleep scale
- PIGD:
-
Postural instability with gait difficulty
- PRI:
-
Power ratio index
- PSD:
-
Power spectrum density
- SD:
-
Standard deviation
- SMA:
-
Supplementary motor area
- TBR:
-
Theta-beta ratio
- TD:
-
Tremor-dominant
- TMS:
-
Transcranial magnetic stimulation
- TUG:
-
Timed up and go test
- UPDRS:
-
Unified Parkinson’s disease rating scale
References
Abos A, Baggio HC, Segura B et al (2019) Differentiation of multiple system atrophy from Parkinson’s disease by structural connectivity derived from probabilistic tractography. Sci Rep 9:16488
Ahmed Z, Mohamed K, Zeeshan S, Dong X (2020) Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database. https://doi.org/10.1093/database/baaa010
Apóstolo J (2011) Adaptation into European Portuguese of the geriatric depression scale (GDS-15). Rev Referência 3
Awate SP, Yushkevich P, Licht D, Gee JC (2009) Gender differences in cerebral cortical folding: multivariate complexity-shape analysis with insights into handling brain-volume differences. Med Image Comput Comput Assist Interv 12:200–207
Bäumer T, Dammann E, Bock F et al (2007) Laterality of interhemispheric inhibition depends on handedness. Exp Brain Res 180:195–203
Beudel M, Roosma E, Martinez Manzanera OE et al (2015) Parkinson bradykinesia correlates with EEG background frequency and perceptual forward projection. Parkinsonism Relat Disord 21:783–788
Boon LI, Geraedts VJ, Hillebrand A et al (2019) A systematic review of MEG-based studies in Parkinson’s disease: the motor system and beyond. Hum Brain Mapp 40:2827–2848
Brazhnik E, Cruz AV, Avila I et al (2012) State-dependent spike and local field synchronization between motor cortex and substantia nigra in hemiparkinsonian rats. J Neurosci 32:7869–7880
Cantillo-Negrete J, Carino-Escobar RI, Carrillo-Mora P et al (2016) Gender differences in quantitative electroencephalogram during a simple hand movement task in young adults. Rev Invest Clin 68:245–255
Cavanagh JF, Frank MJ (2014) Frontal theta as a mechanism for cognitive control. Trends Cogn Sci 18:414–421
Chaudhuri KR, Pal S, DiMarco A et al (2002) The Parkinson’s disease sleep scale: a new instrument for assessing sleep and nocturnal disability in Parkinson’s disease. J Neurol Neurosurg Psychiatry 73:629–635
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
Cheng Y, Lee P-L, Yang C-Y et al (2008) Gender differences in the mu rhythm of the human mirror-neuron system. PLoS ONE 3:e2113
Cozac VV, Gschwandtner U, Hatz F et al (2016) Quantitative EEG and cognitive decline in Parkinson’s disease. Parkinsons Dis 2016:9060649
de Freitas Barbosa VA, Gomes JC, de Santana MA et al (2021) Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests. Res Biomed Eng. https://doi.org/10.1007/s42600-020-00112-5
de Oliveira APS, de Santana MA, Andrade MKS et al (2020) Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence. Res Biomed Eng 36:311–331
de Sousa RL, de Medeiros JGM, de Moura ACL et al (2007) Validade e fidedignidade da Escala de Depressão Geriátrica na identificação de idosos deprimidos em um hospital geral. J Bras Psiquiatr 56:102–107
de Souza RG, dos Santos Lucas e Silva G, dos Santos WP et al (2021) Computer-aided diagnosis of Alzheimer’s disease by MRI analysis and evolutionary computing. Res Biomed Eng 37:455–483
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21
Emek-Savaş DD, Özmüş G, Güntekin B et al (2017) Decrease of delta oscillatory responses in cognitively normal Parkinson’s disease. Clin EEG Neurosci 48:355–364
Espinola CW, Gomes JC, Pereira JMS, dos Santos WP (2021) Vocal acoustic analysis and machine learning for the identification of schizophrenia. Res Biomed Eng 37:33–46
Fereshtehnejad S-M, Romenets SR, Anang JBM et al (2015) New clinical subtypes of Parkinson disease and their longitudinal progression: a prospective cohort comparison with other phenotypes. JAMA Neurol 72:863–873
Gao C, Sun H, Wang T et al (2018) Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson’s disease. Sci Rep 8:1–21
Geraedts VJ, Boon LI, Marinus J et al (2018) Clinical correlates of quantitative EEG in Parkinson disease: a systematic review. Neurology 91:871–883
Gomes JC, Masood AI, de Silva LHS et al (2021) Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences. Sci Rep 11:11545
Gu Q, Zhang H, Xuan M et al (2016a) Automatic classification on multi-modal MRI data for diagnosis of the postural instability and gait difficulty subtype of Parkinson’s disease. J Parkinsons Dis 6:545–556
Gu Y, Chen J, Lu Y, Pan S (2016b) Integrative frequency power of EEG correlates with progression of mild cognitive impairment to dementia in Parkinson’s disease. Clin EEG Neurosci 47:113–117
Hall SD, Prokic EJ, McAllister CJ et al (2014) GABA-mediated changes in inter-hemispheric beta frequency activity in early-stage Parkinson’s disease. Neuroscience 281:68–76
He X, Zhang Y, Chen J et al (2017) Changes in theta activities in the left posterior temporal region, left occipital region and right frontal region related to mild cognitive impairment in Parkinson’s disease patients. Int J Neurosci 127:66–72
Hoops S, Nazem S, Siderowf AD et al (2009) Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease. Neurology 73:1738–1745
Ishii R, Canuet L, Aoki Y et al (2017) Healthy and pathological brain aging: from the perspective of oscillations, functional connectivity, and signal complexity. Neuropsychobiology 75:151–161
Jávor-Duray BN, Vinck M, van der Roest M et al (2017) Alterations in functional cortical hierarchy in Hemiparkinsonian rats. J Neurosci 37:7669–7681
Khedr EM, Al-Fawal B, Abdel Wraith A et al (2019) The effect of 20 Hz versus 1 Hz repetitive transcranial magnetic stimulation on motor dysfunction in Parkinson’s disease: which is more beneficial? J Parkinsons Dis 9:379–387
Khedr EM, Lefaucheur J-P, Hasan AM, Osama K (2021) Are there differences in cortical excitability between akinetic-rigid and tremor-dominant subtypes of Parkinson’s disease? Neurophysiol Clin 51:443–453
Klem GH (1999) The ten-twenty electrode system of the international federation. The international federation of clinical nenrophysiology. Electroencephalogr Clin Neurophysiol Suppl 52:3–6
Kolmancic K, Perellón-Alfonso R, Pirtosek Z et al (2019) Sex differences in Parkinson’s disease: a transcranial magnetic stimulation study. Mov Disord 34:1873–1881
Lang AE, Eberly S, Goetz CG et al (2013) Movement disorder society unified Parkinson disease rating scale experiences in daily living: longitudinal changes and correlation with other assessments. Mov Disord 28:1980–1986
Lichter DG, Benedict RHB, Hershey LA (2021) Freezing of gait in Parkinson’s disease: risk factors, their interactions, and associated nonmotor symptoms. Parkinsons Dis 2021:8857204
Luccas FJ, Anghinah R, Braga NI et al (1999) Guidelines for recording/analyzing quantitative EEG and evoked potentials. Part II: clinical aspects. Arq Neuropsiquiatr 57:132–146
Luders E, Narr KL, Thompson PM et al (2004) Gender differences in cortical complexity. Nat Neurosci 7:799–800
Mestre TA, Fereshtehnejad S-M, Berg D et al (2021) Parkinson’s disease subtypes: critical appraisal and recommendations. J Parkinsons Dis 11:395–404
Morita A, Kamei S, Serizawa K, Mizutani T (2009) The relationship between slowing EEGs and the progression of Parkinson’s disease. J Clin Neurophysiol 26:426–429
Neuper C, Pfurtscheller G (2001) Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas. Clin Neurophysiol 112:2084–2097
Niedermeyer E, da Silva FHL (2005) Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins
Niethammer M, Feigin A, Eidelberg D (2012) Functional neuroimaging in Parkinson’s disease. Cold Spring Harb Perspect Med 2:a009274
Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113
Pan P, Zhang Y, Liu Y et al (2017) Abnormalities of regional brain function in Parkinson’s disease: a meta-analysis of resting state functional magnetic resonance imaging studies. Sci Rep. https://doi.org/10.1038/srep40469
Pandis N (2014) Cross-sectional studies. Am J Orthod Dentofacial Orthop 146:127–129
Pang H, Yu Z, Yu H et al (2021) Use of machine learning method on automatic classification of motor subtype of Parkinson’s disease based on multilevel indices of rs-fMRI. Parkinsonism Relat Disord 90:65–72
Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857
Poewe W, Seppi K, Tanner CM et al (2017) Parkinson disease. Nat Rev Dis Primers 3:17013
Pollok B, Krause V, Martsch W et al (2012) Motor-cortical oscillations in early stages of Parkinson’s disease. J Physiol 590:3203–3212
Possti D, Fahoum F, Sosnik R et al (2021) Changes in the EEG spectral power during dual-task walking with aging and Parkinson’s disease: initial findings using Event-Related Spectral Perturbation analysis. J Neurol 268:161–168
Schrag A, Barone P, Brown RG et al (2007) Depression rating scales in Parkinson’s disease: critique and recommendations. Mov Disord 22:1077–1092
Serizawa K, Kamei S, Morita A et al (2008) Comparison of quantitative EEGs between Parkinson disease and age-adjusted normal controls. J Clin Neurophysiol 25:361–366
Shirahige L, Berenguer-Rocha M, Mendonça S et al (2020) Quantitative electroencephalography characteristics for Parkinson’s disease: a systematic review. J Parkinsons Dis 10:455–470
Shukla S, Thirugnanasambandam N (2021) Deriving mechanistic insights from machine learning and its possible implications in non-invasive brain stimulation research. Brain Stimul 14:1035–1037
Simuni T, Caspell-Garcia C, Coffey C et al (2016) How stable are Parkinson’s disease subtypes in de novo patients: analysis of the PPMI cohort? Parkinsonism Relat Disord 28:62–67
Singh A, Richardson SP, Narayanan N, Cavanagh JF (2018) Mid-frontal theta activity is diminished during cognitive control in Parkinson’s disease. Neuropsychologia 117:113–122
Singh A, Cole RC, Espinoza AI et al (2020) Frontal theta and beta oscillations during lower-limb movement in Parkinson’s disease. Clin Neurophysiol 131:694–702
Sowell ER, Peterson BS, Kan E et al (2007) Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cereb Cortex 17:1550–1560
Soysal A, Sobe I, Atay T et al (2008) Effect of therapy on motor cortical excitability in Parkinson’s disease. Can J Neurol Sci 35:166–172
Spagnolo F, Coppi E, Chieffo R et al (2013) Interhemispheric balance in Parkinson’s disease: a transcranial magnetic stimulation study. Brain Stimul 6:892–897
Stebbins GT, Goetz CG, Burn DJ et al (2013) How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson’s disease rating scale: comparison with the unified Parkinson’s disease rating scale. Mov Disord 28:668–670
Stoffers D, Bosboom JLW, Deijen JB et al (2008) Increased cortico-cortical functional connectivity in early-stage Parkinson’s disease: an MEG study. Neuroimage 41:212–222
Sun D et al (2021) Differentiating Parkinson’s disease motor subtypes: a radiomics analysis based on deep gray nuclear lesion and white matter. Neurosci Lett 760:136083
Tadel F, Baillet S, Mosher JC et al (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011:879716
Tropini G, Chiang J, Wang ZJ et al (2011) Altered directional connectivity in Parkinson’s disease during performance of a visually guided task. Neuroimage 56:2144–2156
Udupa K, Chen R (2013) Motor cortical plasticity in Parkinson’s disease. Front Neurol 4:128
Williams JR, Hirsch ES, Anderson K et al (2012) A comparison of nine scales to detect depression in Parkinson disease: which scale to use? Neurology 78:998–1006
Winkler I, Haufe S, Tangermann M (2011) Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav Brain Funct 7:30
Zarkowski P, Shin CJ, Dang T et al (2006) EEG and the variance of motor evoked potential amplitude. Clin EEG Neurosci 37:247–251
Goetz CG (2012) Unified Parkinson’s Disease Rating Scale (UPDRS) and Movement Disorder Society Revision of the UPDRS (MDS-UPDRS). In: Rating Scales in Parkinson’s Disease, pp 62–83
Meneses MS (2003) Doença de Parkinson. Guanabara Koogan
Wang F, Pan Y, Zhang M, Hu K (2021) Predicting the onset of freezing of gait in de novo Parkinson’s disease. bioRxiv
Winkler I, Haufe S, Mueller K-R (2015) Removal of muscular artefacts for the analysis of brain oscillations: comparison between ICA and SSD. In: ICML workshop on statistics, machine learning and neuroscience (Stamlins 2015)
Witten IH, Frank E, Hall MA et al (2005) Practical machine learning tools and techniques. In: Data mining. p 4
Acknowledgements
We would like to thank Adriana Costa-Ribeiro, Clynton Correa and Érika Rodrigues for their thoughtful suggestions for the article.
Funding
Shirahige L was supported by the Fundação de Amparo à Ciência e Tecnologia de Pernambuco (FACEPE), Brazil (IBPG-1548-4.01/16). Monte-Silva K receives a grant (308291/2015-8) from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Shirahige, L., Leimig, B., Baltar, A. et al. Classification of Parkinson’s disease motor phenotype: a machine learning approach. J Neural Transm 129, 1447–1461 (2022). https://doi.org/10.1007/s00702-022-02552-y
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DOI: https://doi.org/10.1007/s00702-022-02552-y