BioMed Research International

BioMed Research International / 2016 / Article

Research Article | Open Access

Volume 2016 |Article ID 2845754 | 6 pages | https://doi.org/10.1155/2016/2845754

Relation between Resting State Front-Parietal EEG Coherence and Executive Function in Parkinson’s Disease

Academic Editor: Brandon A. Ally
Received12 Nov 2015
Revised11 Apr 2016
Accepted05 Jun 2016
Published28 Jun 2016

Abstract

Objective. To assess the relation between executive dysfunction (ED) in Parkinson’s disease (PD) and resting state functional connectivity evaluated using electroencephalography (EEG) coherence. Methods. Sixty-eight nondemented sporadic PD patients were assessed using the Behavioural Assessment of the Dysexecutive Syndrome (BADS) to evaluate executive function. EEG coherence in the left frontoparietal electrode pair (F3-P3) and the right frontoparietal electrode pair (F4-P4) was analyzed in the alpha and theta range. The BADS scores were compared across the coherence groups, and the multiple logistic regression analysis was performed to assess the contribution of confounders. Results. The standardized BADS score was significantly lower in the low F3-P3 coherence group in the alpha range (Mann-Whitney test, ), though there was no difference between F4-P4 coherence group in the alpha range, F3-P3, and F4-P4 coherence groups in the theta range and the standardized BADS score. The multiple logistic regression analysis revealed the significant relation between the F3-P3 coherence group in alpha range and age-controlled standardized BADS score (, 95% CI = 1.002–1.062). Conclusion. The decrease in resting state functional connectivity between the frontal and parietal cortices especially in the left side is related to ED in PD.

1. Introduction

Cognitive impairment is a common symptom of Parkinson’s disease (PD) [1], and executive dysfunction (ED) is a well-known cognitive impairment in PD [2]. Executive function refers to a set of cognitive processes that control goal-directed behaviors, from goal formulation and intention formation to successful execution and processing of the outcome [3]. ED is a nonmotor symptom of PD and presents in early and late stages of the disease [3]. ED in PD has a negative impact on patients’ quality of life and affects caregiver burden [3, 4]. ED in PD has been studied with many neuropsychological assessments including the Wisconsin Card Sorting Test, Trail Making Test [3], and the Behavioral Assessment of the Dysexecutive Syndrome (BADS) [5]. ED is characterized by deficits in internal control of attention, set shifting, planning, inhibition, conflict resolution, impairment in dual-task performance, and a range of decision-making and social cognition tasks [3].

The underlying mechanism of ED in PD is not clearly understood. Executive function is multifaceted; frontal and parietal cortical regions are reciprocally interconnected with each other and to the basal ganglia and thalamus in executive function [6]. Some studies have suggested that ED in patients with PD is caused by degeneration of the basal ganglia and/or frontal cortex [7, 8]. Neuroimaging studies using task-related fMRI have suggested that executive function in patients with PD is associated with fronto-parietal-striatal networks [6, 9]. Recently, the relation between the functional connectivity of resting state networks and cognitive impairment has been investigated. Tessitore et al. recently found that the default mode network (DMN) was different in 16 cognitively normal PD patients as compared to 16 healthy controls and, in PD, decreases in functional connectivity were located in the temporoparietal cortex within the DMN [10, 11]. Van Eimeren et al. revealed changes specifically in the posterior node of the DMN in seven unmedicated PD patients as compared to seven healthy controls [10, 12]. In PD, a limited number of studies have assessed the relation between resting state functional connectivity and executive function.

EEG coherence is a fundamental hallmark of integrated cortical functions [13]. EEG coherence is often used to assess functional connectivity in the human cortex [14]. A previous study reported that, in a resting state, patients with PD had lower EEG coherence within parietal electrodes at around 10 Hz than healthy controls [13].

In order to disclose changes in functional connectivity in PD patients with ED, this study assessed the relation between ED and EEG coherence in frontal and parietal regions in nondemented patients with PD.

2. Patients and Methods

2.1. Patients

Patients with sporadic PD were consecutively enrolled at the Neurology Clinic of Nihon University Itabashi Hospital between December 2006 and October 2008. The clinical diagnosis of sporadic PD was made according to the UK PD Brain Bank criteria [15]. Patients with other forms of Parkinsonism including drug-induced Parkinsonism, vascular Parkinsonism, dementia with Lewy bodies [16, 17], and atypical Parkinsonism with absent or minimal responses to anti-Parkinsonian drugs were excluded. Dementia with Lewy bodies was defined as the onset of dementia within 1 year of the onset of motor symptoms and did not have a history of visual hallucinations. Cranial magnetic resonance images were obtained from all patients, and patients with ischemic changes including a single lacuna and/or slight periventricular hyperintensity on T2-weighted images and fluid-attenuated inversion recovery images [18] were excluded. Patients who were given drugs that may influence EEG such as antianxiety drugs and psychotropic drugs were excluded.

All patients were assessed using the Mini-Mental State Examination (MMSE) based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) criteria for dementia, in accordance with a previous study [19]. Patients with an MMSE score <24 were excluded.

Executive function was assessed using the BADS [20]. The BADS is composed of six subtests that evaluate various aspects of ED and includes assessment for ED in a wide range of daily activities with ecological validity and is sensitive to ED in patients with PD [2, 5]. ED was defined as an age-controlled standardized score <70.

Written informed consent for participation in the present study was obtained from each patient according to a protocol approved by the Institutional Research Review Board of Nihon University.

2.2. Assessment of EEG Coherence

EEG examinations were performed using a magnetic optical disk with 16 electrode locations according to the International 10/20 System using a digital EEG instrument (Neurofax EEG-1100; Nihon Kohden, Tokyo, Japan). The EEG channels were recorded with a sampling rate of 200 Hz. EEG data were obtained with the patient in a resting awake condition with eyes closed. EEG coherence in the left frontoparietal electrode pair (F3-P3) and the right frontoparietal electrode pair (F4-P4) was analyzed using the EEG Analysis Software EMSE® Suite version 5.5.2 (SSI Inc., La Mesa, CA). Waves with a frequency from 1 Hz to 31 Hz were analyzed with a bandpass filter. The analysis window duration was 1 s with 80% overlapping. The data were divided into four frequency bands: delta (1.56–3.12 Hz), theta (3.90–7.80 Hz), alpha (8.59–12.48 Hz), and beta (13.26–29.64 Hz). Coherence was calculated in the alpha and theta range. Patients were divided into two groups based on the EEG coherence: low coherence (coherence <0.5) and high coherence (coherence ≥0.5).

2.3. Statistical Analysis

IBM SPSS statistics for Windows version 22 (IBM Corp., Armonk, NY) was used for statistical analysis. The Shapiro-Wilk normality test was used to evaluate whether continuous variables exhibited a normal distribution and nonparametric analysis was used for all nonnormal data. Demographic, clinical, and neuropsychological variables were compared across the coherence groups. Patient age was classified as ≤50 years, 51–60 years, 61–70 years, or >70 years. Patient age, sex, and Hoehn and Yahr (HY) stage were compared across the two coherence groups using Fisher’s exact test. Disease duration, standardized BADS score, and age-controlled standardized BADS score were compared across the coherence groups using the Mann-Whitney test. The relation between coherence group (low, high) and ED (present, absent) was assessed by Fisher’s exact test. When the significant relation between the two coherence groups and standardized BADS score and/or age-controlled standardized BADS score was found, multiple logistic regression analysis was done to assess the contribution of confounders (age distribution, sex, disease duration, and distribution of HY stage). The level of statistical significance was defined as 0.05 for all tests.

3. Results

Eighty-nine patients with PD were enrolled and analyzed EEG coherence. However, 21 patients with MMSE score below 24 points were excluded. Characteristics of the 68 patients with MMSE score over 24 points are shown in Table 1. Median HY stage for all enrolled patients was 3.0.


Number of patients68

Age (years)68.0 (34–87)
 ≤506
 51–6015
 61–7022
 >7025

Sex
 Male35
 Female33

Disease duration (months)60 (9–228)

HY stage3.0 (1–5)
 I4
 II31
 III28
 IV4
 V1

Standardized BADS score87.5 (46–124)

Age-controlled standardized BADS score93 (53–124)

Data are expressed as median (range) or .
BADS: Behavioral Assessment of the Dysexecutive Syndrome; HY stage: Hoehn and Yahr stage.

Table 2 shows the relation between coherence and ED in PD patients. In alpha range, there was a significant difference in the distribution of PD patients with ED between high F3-P3 coherence group and low F3-P3 coherence group (Fisher’s exact test, ). There was a significant difference in the distribution of PD patients with ED between high F4-P4 coherence group and low F4-P4 coherence group (Fisher’s exact test, ). ED was significantly more frequent in low coherence group in alpha range. In theta range, there were no significant differences in the distribution of PD patients with ED between high coherence group and low coherence group.


RangeAge-controlled standardized BADS score
<70 (patients with ED)≥70 (patients without ED)

AlphaLow F3-P3 (left side) coherence group 13260.016
High F3-P3 (left side) coherence group227
Low F4-P4 (right side) coherence group 11220.041
High F4-P4 (right side) coherence group431

ThetaLow F3-P3 (left side) coherence group 12380.742
High F3-P3 (left side) coherence group315
Low F4-P4 (right side) coherence group 8320.768
High F4-P4 (right side) coherence group721

Data are expressed as number of patients.
BADS: Behavioral Assessment of the Dysexecutive Syndrome; ED: executive dysfunction; PD: Parkinson’s disease.
ED was defined as age-controlled standardized BADS score <70.
The low coherence group had coherence <0.5 and the high coherence group had coherence ≥0.5.
The relation between two coherence groups and ED was assessed using Fisher’s exact test. Statistically significant ().

Table 3 shows relation between EEG alpha coherence in the frontoparietal electrode pair and clinical characteristics in PD patients. Age distribution, sex, distribution of HY stage, and disease duration were not significantly different between high F3-P3 coherence group and low F3-P3 coherence group. The standardized BADS score was significantly different between high F3-P3 coherence group and low F3-P3 coherence group (Mann-Whitney test, ). The standardized BADS score was lower in the low F3-P3 coherence group. On the other hand, the standardized BADS score was not significantly different between high F4-P4 coherence group and low F4-P4 coherence group (Mann-Whitney test, ).


Low F3-P3 (left side)
coherence group
High F3-P3 (left side)
coherence group
Low F4-P4 (right side)
coherence group
High F4-P4 (right side)
coherence group

Age (years)70.0 (42–87)61.5 (34–81)70.5 (42–87)59.0 (34–76)
 ≤50240.144150.002
 51–60710314
 61–70139139
 >70187178

Sex
 Male23140.46918191.00
 Female17161617

Disease duration (months)48 (9–192)60 (9–228)0.13050 (9–192)60 (9–228)0.300

HY stage3.0 (1–5)2.0 (1–4)
 I320.464140.012
 II15161219
 III199208
 IV2314
 V1001

Standardized BADS score85 (46–114)95 (56–124)0.03285 (51–114)95 (46–124)0.055

Age-controlled standardized BADS score89 (54–124)94 (53–124)0.06384 (59–124)94 (53–124)0.152

Data are expressed as median (range) or .
BADS: Behavioral Assessment of the Dysexecutive Syndrome; HY stage: Hoehn and Yahr stage; PD: Parkinson’s disease.
The low coherence group had coherence <0.5 and the high coherence group had coherence ≥0.5.
Age, sex, and HY stage were compared across groups using Fisher’s exact test. Disease duration, standardized BADS score, and age-controlled standardized BADS score were compared across groups using the Mann-Whitney test. Statistically significant ().

The multiple logistic regression analysis revealed the significant relation between the F3-P3 coherence group in alpha range and age-controlled standardized BADS score (, odds ratio = 1.03, 95% CI = 1.002–1.062). Other factors, including patients’ age distribution, sex, disease duration, and distribution of HY stage, were not significant (Table 4). The relation between the F4-P4 coherence group in alpha range and the standardized BADS score using the multiple logistic regression analysis was not statistically significant (, 95% CI = 0.9997–1.057).


SEOdds ratio95% CI

Age
 ≤50 years = 1−0.420.301.860.170.660.36–1.20
 51 to 60 years = 2
 61 to 70 years = 3
 >70 years = 4
Sex
 Male = 1, female = 0−1.030.563.450.0630.360.12–1.06
Disease duration
 Month, real number0.00400.00570.500.481.000.99–1.02
HY stage
 Real number−0.240.430.310.580.790.34–1.82
Age-controlled standardized BADS score
 Real number0.0310.0154.250.0391.031.002–1.062

A dichotomous dependent variable of EEG alpha coherence in the left frontoparietal electrode pair was assigned a value of 0 when coherence was <0.5 and 1 when coherence was ≥0.5.
BADS: Behavioral Assessment of the Dysexecutive Syndrome; HY stage: Hoehn and Yahr stage; : regression coefficient; SE: standard error; 95% CI; 95% confidence interval.
Statistically significant ().

4. Discussion

We assessed the relation between ED and resting state EEG coherence in the alpha and theta frequency range in nondemented patients with PD. Our results showed a difference in EEG coherence in alpha range between nondemented PD patients with and without ED and no significant difference in theta range. EEG data were obtained with the patient in a resting awake condition with eyes closed and alpha wave recorded mainly in this state, in general. This might cause no significant difference of coherence between nondemented PD patients with and without ED in theta range. Low EEG coherence between the frontal and parietal region in alpha frequency range was associated with poor executive task performance. EEG is directly related to dynamic postsynaptic activity in the cortex [21] and has a wide spectrum of clinical applications [22]. A previous study reported that, in a resting state, patients with PD had lower EEG coherence within parietal electrodes at around 10 Hz than healthy controls [13]. Another study reported differences in the resting state EEG coherence pattern in the alpha frequency range between healthy subjects and patients with Alzheimer’s disease, PD, and PD with dementia [22]. The frequency of the patients with ED was significantly higher in low coherence group in alpha, not only on the left side but also on the right side; however, the multiple logistic regression analysis revealed the significant relation between coherence in alpha range and age-controlled standardized BADS score only in the left side. Relation between ED and laterality of functional connectivity in PD was unclear. Rektorova reported significant correlation between functional connectivity of bilateral inferior parietal cortex within DMN and attention/executive function in PD patients [10]. Baggio et al. reported reduction of connectivity between the dorsal attention network and right frontoinsular regions associated with worse performance in attention/executive functions in mild cognitive impairment PD patients [23]. Our result suggested ED in PD is associated with a reduction of resting state functional connectivity in the frontal and parietal cortices, especially on the left side.

fMRI is one of the tools used to assess resting state functional connectivity and can be used to detect coherent fluctuations of blood-oxygenation-level-dependent signals [24, 25]. An fMRI study suggested that, in healthy subjects, executive task performance correlated with resting state lateral parietal nodes in a network that links the dorsolateral frontal and parietal cortices [25]. It has been suggested that the topological properties of brain networks are altered in PD patients with mild cognitive impairment, including dysfunction of executive function, attention, visuospatial functions, and memory. These findings have been obtained using graph-theoretical analyses of functional networks obtained with resting state fMRI [23]. In PD patients who were cognitively unimpaired, the decreased DMN connectivity significantly correlated with cognitive parameters but not with disease duration, motor impairment, or levodopa therapy [11].

The fMRI signal is only an indirect measure of neuronal activity [26], and fMRI is expensive to operate. The time resolution of EEG is in the range of milliseconds [27]. In addition, EEG is of low cost and represents no risk to the patient [22]. EEG coherence is therefore a good tool with which to assess changes in functional connectivity in PD.

This study revealed that low EEG coherence between the left frontal and left parietal region was associated with poor executive task performance in PD. A decrease in resting state functional connectivity between the frontal and parietal cortices is related to ED in PD.

Competing Interests

The authors declared no competing interests.

Acknowledgments

This work was supported by a grant from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) Supported Program for the Strategic Research Foundation at Private Universities, 2014 (S1291004).

References

  1. N. S. Narayanan, R. L. Rodnitzky, and E. Y. Uc, “Prefrontal dopamine signaling and cognitive symptoms of Parkinson's disease,” Reviews in the Neurosciences, vol. 24, no. 3, pp. 267–278, 2013. View at: Publisher Site | Google Scholar
  2. B. Perfetti, S. Varanese, P. Mercuri, E. Mancino, A. Saggino, and M. Onofrj, “Behavioural assessment of dysexecutive syndrome in Parkinson's disease without dementia: a comparison with other clinical executive tasks,” Parkinsonism and Related Disorders, vol. 16, no. 1, pp. 46–50, 2010. View at: Publisher Site | Google Scholar
  3. G. Dirnberger and M. Jahanshahi, “Executive dysfunction in Parkinson's disease: a review,” Journal of Neuropsychology, vol. 7, no. 2, pp. 193–224, 2013. View at: Publisher Site | Google Scholar
  4. A. Kudlicka, L. Clare, and J. V. Hindle, “Quality of life, health status and caregiver burden in Parkinson's disease: relationship to executive functioning,” International Journal of Geriatric Psychiatry, vol. 29, no. 1, pp. 68–76, 2014. View at: Publisher Site | Google Scholar
  5. S. Kamei, M. Hara, K. Serizawa et al., “Executive dysfunction using behavioral assessment of the dysexecutive syndrome in Parkinson's disease,” Movement Disorders, vol. 23, no. 4, pp. 566–573, 2008. View at: Publisher Site | Google Scholar
  6. L. Gawrys, M. Falkiewicz, A. Pilacinski et al., “The neural correlates of specific executive dysfunctions in Parkinson’s disease,” Acta Neurobiologiae Experimentalis, vol. 74, no. 4, pp. 465–478, 2014. View at: Google Scholar
  7. O. Monchi, M. Petrides, J. Doyon, R. B. Postuma, K. Worsley, and A. Dagher, “Neural bases of set-shifting deficits in Parkinson's disease,” Journal of Neuroscience, vol. 24, no. 3, pp. 702–710, 2004. View at: Publisher Site | Google Scholar
  8. A. M. Owen, J. Doyon, A. Dagher, A. Sadikot, and A. C. Evans, “Abnormal basal ganglia outflow in Parkinson's disease identified with PET. Implications for higher cortical functions,” Brain, vol. 121, part 5, pp. 949–965, 1998. View at: Publisher Site | Google Scholar
  9. N. J. H. M. Gerrits, Y. D. van der Werf, K. M. W. Verhoef et al., “Compensatory fronto-parietal hyperactivation during set-shifting in unmedicated patients with Parkinson's disease,” Neuropsychologia, vol. 68, pp. 107–116, 2015. View at: Publisher Site | Google Scholar
  10. I. Rektorova, “Resting-state networks in Alzheimer's disease and Parkinson's disease,” Neurodegenerative Diseases, vol. 13, no. 2-3, pp. 186–188, 2014. View at: Publisher Site | Google Scholar
  11. A. Tessitore, F. Esposito, C. Vitale et al., “Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease,” Neurology, vol. 79, no. 23, pp. 2226–2232, 2012. View at: Publisher Site | Google Scholar
  12. T. Van Eimeren, O. Monchi, B. Ballanger, and A. P. Strafella, “Dysfunction of the default mode network in Parkinson disease: a functional magnetic resonance imaging study,” Archives of Neurology, vol. 66, no. 7, pp. 877–883, 2009. View at: Publisher Site | Google Scholar
  13. M. Moazami-Goudarzi, J. Sarnthein, L. Michels, R. Moukhtieva, and D. Jeanmonod, “Enhanced frontal low and high frequency power and synchronization in the resting EEG of parkinsonian patients,” NeuroImage, vol. 41, no. 3, pp. 985–997, 2008. View at: Publisher Site | Google Scholar
  14. R. Srinivasan, W. R. Winter, J. Ding, and P. L. Nunez, “EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics,” Journal of Neuroscience Methods, vol. 166, no. 1, pp. 41–52, 2007. View at: Publisher Site | Google Scholar
  15. W. R. G. Gibb and A. J. Lees, “The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease,” Journal of Neurology, Neurosurgery and Psychiatry, vol. 51, no. 6, pp. 745–752, 1988. View at: Publisher Site | Google Scholar
  16. I. G. McKeith, D. Galasko, K. Kosaka et al., “Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): Report of the consortium on DLB international workshop,” Neurology, vol. 47, no. 5, pp. 1113–1124, 1996. View at: Publisher Site | Google Scholar
  17. F. Geser, G. K. Wenning, W. Poewe, and I. McKeith, “How to diagnose dementia with Lewy bodies: state of the art,” Movement Disorders, vol. 20, no. 12, pp. S11–S20, 2005. View at: Publisher Site | Google Scholar
  18. F. Fazekas, J. B. Chawluk, A. Alavi, H. I. Hurtig, and R. A. Zimmerman, “MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging,” American Journal of Roentgenology, vol. 149, no. 2, pp. 351–356, 1987. View at: Publisher Site | Google Scholar
  19. M. F. Folstein, S. E. Folstein, and P. R. McHugh, “‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician,” Journal of Psychiatric Research, vol. 12, no. 3, pp. 189–198, 1975. View at: Publisher Site | Google Scholar
  20. B. A. Wilson, N. Alderman, P. W. Burgess, H. Emslie, and J. J. Evans, Behavioural Assessment of the Dysexecutive Syndrome, Thames Valley Test Company, Bury St Edmunds, UK, 1996.
  21. M. Murias, S. J. Webb, J. Greenson, and G. Dawson, “Resting state cortical connectivity reflected in EEG coherence in individuals with autism,” Biological Psychiatry, vol. 62, no. 3, pp. 270–273, 2007. View at: Publisher Site | Google Scholar
  22. L. C. Fonseca, G. M. A. S. Tedrus, P. N. Carvas, and E. C. F. A. Machado, “Comparison of quantitative EEG between patients with Alzheimer's disease and those with Parkinson's disease dementia,” Clinical Neurophysiology, vol. 124, no. 10, pp. 1970–1974, 2013. View at: Publisher Site | Google Scholar
  23. H.-C. Baggio, R. Sala-Llonch, B. Segura et al., “Functional brain networks and cognitive deficits in Parkinson's disease,” Human Brain Mapping, vol. 35, no. 9, pp. 4620–4634, 2014. View at: Publisher Site | Google Scholar
  24. A. Tessitore, M. Amboni, F. Esposito et al., “Resting-state brain connectivity in patients with Parkinson's disease and freezing of gait,” Parkinsonism and Related Disorders, vol. 18, no. 6, pp. 781–787, 2012. View at: Publisher Site | Google Scholar
  25. W. W. Seeley, V. Menon, A. F. Schatzberg et al., “Dissociable intrinsic connectivity networks for salience processing and executive control,” Journal of Neuroscience, vol. 27, no. 9, pp. 2349–2356, 2007. View at: Publisher Site | Google Scholar
  26. W. Richter and M. Richter, “The shape of the fMRI BOLD response in children and adults changes systematically with age,” NeuroImage, vol. 20, no. 2, pp. 1122–1131, 2003. View at: Publisher Site | Google Scholar
  27. C. Mulert, L. Jäger, R. Schmitt et al., “Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection,” NeuroImage, vol. 22, no. 1, pp. 83–94, 2004. View at: Publisher Site | Google Scholar

Copyright © 2016 Hiroko Teramoto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

1003 Views | 544 Downloads | 8 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.