543796 research-article2014
EEGXXX10.1177/1550059414543796Clinical EEG and NeuroscienceGu et al
Original Article
Integrative Frequency Power of EEG Correlates with Progression of Mild Cognitive Impairment to Dementia in Parkinson’s Disease
Clinical EEG and Neuroscience 1–5 © EEG and Clinical Neuroscience Society (ECNS) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1550059414543796 eeg.sagepub.com
Youquan Gu1,2, Jun Chen2, Yaqin Lu2, and Suyue Pan1
Abstract Clinically, predicting the progression of mild cognitive impairment (MCI) and diagnosing dementia in Parkinson’s disease (PD) are difficult. This study aims to explore an integrative electroencephalography (EEG) frequency power that could be used to predict the progression of MCI in PD patients. Twenty-six PD patients, in this study, were divided into the mild cognitive impairment group (PDMCI, 17 patients) and dementia group (PDD, 9 patients) according to cognitive performance. Beta peak frequency, alpha relative power, and alpha/theta power were recorded and analyzed for the prediction. Mini Mental State Examination (MMSE) scores at initiation, in the first year, and in the second year were examined. The sensitivity, specificity, positive predictive value, Matthew correlation coefficient, and positive likelihood ratio were calculated in both the integrative EEG biomarkers and single best biomarker. Of the 17 patients with MCI for 2 years, 6 progressed to dementia. Integrative EEG biomarkers, mainly associated with beta peak frequency, can predict conversion from MCI to dementia. These biomarkers had sensitivity of 82% and specificity of 78%, compared with sensitivity of 61% and specificity of 58% of the beta peak frequency. In conclusion, the integrative EEG frequency powers were more sensitive and specific to MCI progression in PD patients. Keywords Parkinson’s disease, mild cognitive impairment, dementia, electroencephalography, progression Received May 11, 2014; revised May 26, 2014; accepted May 28, 2014.
Introduction Clinically, therapy for Parkinson’s disease (PD) treats most of the motor symptoms.1,2 However, nonmotor symptoms also pose difficulties in advanced disease. The most common nonmotor symptom with PD is mild cognitive impairment (PDMCI). Caviness et al.3 found, and defined for the first time, MCI as a stage between normal cognition and PD dementia (PDD). In recent years, many scientists have studied MCI in PD patients.4,5 The prevalence of PDMCI varies between 19% and 39%, depending on criteria and study population, and 60% of PDMCI patients develop PDD within 4 years.5,6 However, there are no effective biomarkers for the progression of MCI in PD. Patients with PD are at high risk of developing MCI. The therapies that stop the conversion to MCI unfortunately remain to be developed, but it is likely that these therapies will appear in the future.3,4 It is plausible that these therapies will be most effective before the main cognitive impairment has occurred, and it is, therefore, important to develop biomarkers sensitive in the initial stage of PD.7 Early identification may help the development of new treatments, that are more effective at this stage, as it can facilitate monitoring of the response to the intervention.8
In the present study, we focused on biomarkers obtained from EEG recordings in the eyes-closed resting state. As is known, EEG power bands can be obtained easily, relative cheaply, and with noninvasive equipment. Actually, many studies have investigated EEG powers as biomarkers for neurodegenerative or cerebral injury disease, such as Alzheimer’s disease9 and cerebral hemorrhage.10 We have studied several classical EEG biomarkers, such as the 5 kinds of frequency and powers. By integrating biomarkers, it is always possible to find more optimized separation boundaries for the progression between the normal and the PDMCI patients.
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Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China 2 Department of Neurology, First Hospital of Lanzhou University, Lanzhou, China Corresponding Author: Suyue Pan, Department of Neurology, Nanfang Hospital, Southern Medical University, No.1838, Guangzhoudadaobei Road, Guangzhou 510515, China. Email:
[email protected] Full-color figures are available online at http://eeg.sagepub.com
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Clinical EEG and Neuroscience
Table 1. Description Characteristics of Study Sample and the 2 Cognitive Subgroups.a
Table 2. Overview of the Patients Groups Using MMSE scores.
Groups
Patient Group
MMSE Initial 27 (17) 25 (9)
Gender (female:male) Age in years (range) Education (years) MMSE score
MMSE 1-Year MMSE 2-Year Follow-up Follow-up
PDMCI
PDD
P
5:12 61.72 (31.2-72.31) 10.4 27 ± 1.82
2:07 64.48 (45.31-75.91) 11.3 25 ± 1.18
.0671 .0714
PDMCI (no. of patients) PDD (no. of patients)
.204 .305
Abbreviations: PDMCI, mild cognitive impairment in Parkinson’s disease; PDD, dementia in Parkinson’s disease; MMSE, Mini Mental State Examination.
Abbreviations: PDMCI, mild cognitive impairment in Parkinson’s disease; PDD, dementia in Parkinson’s disease; MMSE, Mini Mental State Examination. a P value represents the differences between the 2 subgroups.
Materials and Methods Subjects The present study was approved by the ethics committee of the Southern Medical University, Guangzhou, China. All subjects gave their consent in writing or orally. Twenty-six consecutive patients, who had been referred to the movement disorders clinic of the Department of Neurology of the Southern Medical University, Guangzhou, China, between 2010 and 2013, participated. The clinical data of the patients are described in Table 1. PD in this study was diagnosed according to United Kingdom Parkinson’s Disease Brain Bank criteria.11 Exclusion criteria were: use of neuroleptic drugs, drug or alcohol abuse, history of stroke or other known illnesses of the central nervous system, and any other severe illness.
Neuropsychological Assessment The patients were divided into 2 groups according to cognitive performance and activities of daily living; mild cognitive impairment (PDMCI, 17 patients) and dementia (PDD, 9 patients). Because of Chinese characteristics, the diagnosis of MCI was based on criteria, set by Petersen et al,12 which consist of objective memory impairment determined by neuropsychological evaluation, and defined by performances ≥1.5 standard deviations below the mean value for level of education. On their first visit to the Neurology Department, all patients underwent a thorough 1-day examination consisting of history taking, physical and neurological assessment, and neuropsychological testing including the MMSE,13 structural magnetic resonance imaging (MRI), and routine EEG.
EEG Recordings EEG was recorded with 32 electrodes using standard EEG electrode placement. Visual analysis was performed on standard bipolar montages according to the 10-20 international system (Fp2, Fp1, FT9, FT10, F8, F7, F4, F3, A2, A1, T4, T3, C4, C3, T6, T5, P4, P3, O2, O1, Fz, Cz, and Pz). The recording was referenced to the common average of all electrodes, excluding Fp1 and Fp2. Detailed EEG examination was performed according to the report by Poil et al.8 During the EEG,
27 (14) 23 (12)
27 (11) 21 (15)
subjects kept their eyes closed most of the time. Recordings during eyes-open were not analyzed. However, at irregular intervals, patients were asked to open their eyes when drowsiness was noticed. Eye movements, eye blinks, muscle artifacts and heartbeat components were rejected, based on abnormal topography, component activation, activity distribution, and spectrum.
Statistical Analysis All groups were compared for age, gender, education, MMSE score, and motor stage with the Kurskal–Wallis and Mann– Whitney U test. Spearman rank was used to analyze the correlation between MMSE score and EEG abnormalities.
Results Effects of Gender and Age on the Cognitive Impairment Changes Initially, 17 patients were diagnosed with MCI, and 9 patients with dementia. On Table 1, there are no significant differences with regard to age and gender between the PDMCI and PDD group (gender P = .671; age P = .0714).
Different MMSE Scores Between Two Groups From Tables 1 and 2, it is found that the MMSE score of the PDMCI (27 ± 1.82) was not significantly different from the score of the PDD group (25 ± 1.18) at the initial test (P = .305). At the follow-up about 1 year later, PDMCI patients remained at a stable MMSE score of 27 ± 2.06, whereas the MMSE score of the PDD group changed to 23 ± 1.64, which is also lower than the PDMCI group’s MMSE scores (P = .0314). At the follow-up 2 years later, the MMSE score of PDMCI patients was also 27 ± 1.87, but the MMSE score of PDD patients was 21 ± 1.54. The MMSE score of the PDD group was significantly lower compared with that of the PDMCI group (P = .012).
Patients with MCI Convert to Patients with PD Assessed by MMSE After 1 and 2 years, the number of patients with PDMCI decreased and the number of patients with PDD increased. According to Table 2, after one year 3 patients with MCI converted to PDD with the MMSE score from the baseline of 27 to 25. These 3 patients are included in the PDD group, whose
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Figure 1. Beta peak frequency, alpha relative power, and alpha/theta ratio changes in PDMCI and PDD group in the first and second years. Abbreviations: PDMCI, mild cognitive impairment in Parkinson’s disease; PDD, dementia in Parkinson’s disease; EEG, electroencephalography. *P < .05, **P < .01, and ***P < .001 represent the EEG frequency in the PDD group compared with the PDMCI group.
MMSE score decreased from 25 to 23. In the second year after initial MMSE 3 patients with MCI converted to PDD. These 3 patients are also included in the PDD group, whose MMSE score decreased from 23 to 21. However, the MMSE scores of the remaining MCI patients have not changed, with 27 scores in first and second year.
Band Powers Significantly Changed at the First- and Second-Year Follow-up We observed the band powers in 14 patients in the PDMCI group and 12 patients in the PDD group at the first year, and the band powers in 11 patients in the PDMCI group and 15 patients in the PDD group at the second year of follow-up. The results indicated that the beta peak frequency was increased significantly in PDD compared with PDMCI (Figure 1A, P < .01) in both the first and second year follow-up. Meanwhile, the other biomarkers, such as alpha relative power and alpha/ theta, were also decreased in PDD compared with PDMCI (Figure 1B and C, P < .05).
Three Integrative EEG Biomarkers Enhance the Diagnosis Sensitivity and Specificity MMSE scores were employed as the standard marker for the MCI definition. Therefore, we compared the sensitivity and
specificity of the integrative biomarkers with MMSE criteria. The results indicated a good classification with a sensitivity of 82%, specificity of 78%, positive predictive value of 62, Matthew correlation coefficient of 0.58, and positive likelihood ratio of 4.4 (Figure 2), suggesting that the diagnostic index can generally be used for patients who may convert from PDMCI to PDD. Meanwhile, the best of the studied biomarkers was the peak width of the dominant beta peak, with a sensitivity of 61%, specificity of 58%, positive predictive value of 55, Matthew correlation coefficient of 0.31, and positive likelihood ratio of 2.3. We found that the 3 integrative EEG biomarkers were more effective for the MCI diagnosis than the single biomarker.
Discussion In this study, we integrated EEG biomarkers for MCI diagnosis, and found that EEG frequency powers correlated with cognitive dysfunction in PD patients. We showed that the integrative EEG, originally developed for the differentiation of patients with PDD and PDMCI, could be useful in the differentiation of cognitive subgroups in PD. This is probably because band powers reflect those EEG aspects that are altered in cognitively impaired PD patients.14,15 Thus, the integrative EEG frequency powers were found to be associated with conversion from PDMCI to PDD.
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Figure 2. Sensitivity and specificity of the integrative EEG frequency biomarkers and single best biomarker for the mild cognitive impairment progression. The percentages are displayed on the top of the bands.
We explored the prediction of PDMCI converting to PDD within 2 years. For this purpose, we investigated the added value of integrating multiple EEG frequency biomarkers into a diagnostic index, using logistic regression for biomarker selection. We found that biomarkers sensitive to changes in the beta frequency (13-30 Hz) were optimal for classifying the very early EEG recording of yet-to-be diagnosed PDD patients. However, we speculated that combining several different biomarkers may obtain better classification than individual biomarkers.16,17 We thought that such a procedure could result in an applicable diagnosis. The drawback of the present study is the low number of patients, and this may produce a fairly high error margin in the classification evaluations. Alpha relative power and theta/alpha power ratio were also employed to reflect early changes of the well-known slowing of EEG in PD. However, the present optimal set of biomarkers is derived from the beta frequency band (13-30 Hz), which is consistent with previous studies.1,18 The larger width of the beta peak and bandwidth could potentially be linked with a less stable beta frequency and, therefore, also a less efficient working memory.19 Our results show that integrative frequency power has better sensitivity (82% vs 61%) and specificity (78% vs 58%) than beta peak power alone. In conclusion, the noninvasive character of EEG biomarkers could make them a diagnostic index, to provide early-stage clinical assessment of PDMCI converting to PDD. The integrative biomarkers could be more sensitive and specific to MCI progression for patients with PD. Declaration of Conflicting Interests The author(s) declared no conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
References 1. Chung CC, Kang JH, Yuan RY, et al. Multiscale entropy analysis of electroencephalography during sleep in patients with Parkinson disease. Clin EEG Neurosci. 2013;44:221-226. 2. Fahn S, Oakes D, Shoulson, et al; Parkinson Study Group. Levodopa and the progression of Parkinson’s disease. N Engl J Med. 2004;351:2498-2508. 3. Caviness JN, Driver-Dunckley E, Connor DJ, et al. Defining mild cognitive impairment in Parkinson’s disease. Mov Disord. 2007;22:1272-1277. 4. Fonseca LC, Tedrus GM, Letro GH, Bossoni AS. Dementia, mild cognitive impairment and quantitative EEG in patients with Parkinson’s disease. Clin EEG Neurosci. 2009;40: 168-172. 5. Aarsland D, Bronnick K, Williams-Gray, et al. Mild cognitive in Parkinson disease: a multicenter pooled analysis. Neurology. 2010;75:1062-1069. 6. Janvin CC, Larsen JP, Aarsland D, Hugdahl K. Subtypes of mild cognitive impairment in Parkinson’s disease: progression to dementia. Mov Disord. 2006;21:1343-1349. 7. Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendation from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:280-292. 8. Poil SS, de Haan W, van der Flier WM, Mansvelder HD, Scheltens P, Linkenkaer-Hansen K. Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Front Aging Neurosci. 2013;5:58. 9. Kanda PA, Trambaiolli LR, Lorena AC, et al. Clinician’s road map to wavelet EEG as an Alzheimer’s disease biomarker. Clin EEG Neurosci. 2014;45:104-112. 10. Zeng K, Wu XD, Cai HD, et al. Relationship between EEG beta power abnormality and early diagnosis of cognitive impairment post cerebral hemorrhage. Clin EEG Neurosci. 2013;44: 203-208. 11. de Weerd A, Perquin W, Jonkman E. Role of the EEG in the prediction of dementia in Parkinson’s disease. Dementia. 1990;1:115-118. 12. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303-308.
Downloaded from eeg.sagepub.com at Royal Manchester on January 3, 2015
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Gu et al 13. Folstein MF, Folatein SE, McHugh PR. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189-198. 14. Domitrz I, Friedman A. Electroencephalography of demented and non-demented Parkinson’s disease patients. Parkinsonism Relat Disord. 1999;5:37-41. 15. Soikkeli R, Partanen J, Soininen H, Paakkonen A, Riekkinen P. Slowing of EEG in Parkinson’s disease. Electroencephalogr Clin Neurophysiol. 1991;79:159-165. 16. Hanaoka A, Kikuchi M, Komuro R, Oka H, Kidani T, Ichikawa S. EEG coherence analysis in never-medicated patients with panic disorder. Clin EEG Neurosci. 2005;36:42-48.
17. Buscema M, Rossini P, Babiloni C, Grossi E. The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer’s disease patients with high degree of accuracy. Artif Intell Med. 2007;40: 127-141. 18. Yuvaraj R, Murugappan M, Mohamed Ibrahim N, et al. On the analysis of EEG power, frequency and asymmetry in Parkinson’s disease during emotion processing. Behav Brain Funct. 2014; 10:12. 19. Kopell N, Whittington MA, Kramer MA. Neuronal assembly dynamics in the beta 1 frequency range permits short-term memory. Proc Natl Acad Sci U S A. 2011;108:3779-3784.
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