Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram

[21] Glaab E(2013)A novel approach to active compounds identification based on support vector regression model and mean impact value Chem Biol Drug Des 81 650-657

[22] Trezzi JP(2018)Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective d ata analysis using support vector machine Br J Clin Pharmacol 84 2615-2624

[23] Greuel A(2018)Changes in cortical thickness in patients with early Parkinson’s disease at different Hoehn and Yahr stages Front Hum Neurosci 12 469-1955

[24] Kalia LV(2013)Relationship between cognitive impairment and white-matter alteration in Parkinson's disease with dem entia: tract-based spatial statistics and tract-specific analysis Eur Radiol 23 1946-1019

[25] Lanskey JH(2016)Cortical phosphorylated alpha-Synuclein levels correlate with brain wave spectra in Parkinson’s disease Mov Disord 31 1012-1723

[26] Mccolgan P(2011)Brain hodotopy: from esoteric concept to practical surgical applications Neurosurgery 68 1709-943

[27] Schrag AE(2020)Changes of brain structural network connection in Parkinson’s disease patients with mild cognitive dy sfunction: a study based on diffusion tensor imaging J Neurol 267 933-166

[28] Svenningsson P(2018)Machine learning for predicting cognitive diseases: methods, data sources and risk factors J Med Syst 42 243-75

[29] Westman E(2018)Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose PLoS One 13 e0205872-629

[30] Ballard C(2019)The democratic aspect of machine learning: limitations and opportunities for Parkinson’s disease Mov Disord 34 164-1260

[21] Glaab E(2013)A novel approach to active compounds identification based on support vector regression model and mean impact value Chem Biol Drug Des 81 650-657

[22] Trezzi JP(2018)Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective d ata analysis using support vector machine Br J Clin Pharmacol 84 2615-2624

[23] Greuel A(2018)Changes in cortical thickness in patients with early Parkinson’s disease at different Hoehn and Yahr stages Front Hum Neurosci 12 469-1955

[24] Kalia LV(2013)Relationship between cognitive impairment and white-matter alteration in Parkinson's disease with dem entia: tract-based spatial statistics and tract-specific analysis Eur Radiol 23 1946-1019

[25] Lanskey JH(2016)Cortical phosphorylated alpha-Synuclein levels correlate with brain wave spectra in Parkinson’s disease Mov Disord 31 1012-1723

[26] Mccolgan P(2011)Brain hodotopy: from esoteric concept to practical surgical applications Neurosurgery 68 1709-943

[27] Schrag AE(2020)Changes of brain structural network connection in Parkinson’s disease patients with mild cognitive dy sfunction: a study based on diffusion tensor imaging J Neurol 267 933-166

[28] Svenningsson P(2018)Machine learning for predicting cognitive diseases: methods, data sources and risk factors J Med Syst 42 243-75

[29] Westman E(2018)Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose PLoS One 13 e0205872-629

[30] Ballard C(2019)The democratic aspect of machine learning: limitations and opportunities for Parkinson’s disease Mov Disord 34 164-1260