Levodopa May Affect Cortical Excitability in Parkinson's Disease

Neurobiology of Aging 73 (2019) 9e20

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Neurobiology of Aging

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Levodopa may affect cortical excitability in Parkinson’s disease patients with cognitive deﬁcits as revealed by reduced activity of cortical sources of resting state electroencephalographic rhythms

Claudio Babiloni a,b,*, Claudio Del Percio c, Roberta Lizio a,b, Giuseppe Noce c, Susanna Lopez a, Andrea Soricelli c,d, Raffaele Ferri e, Maria Teresa Pascarelli e, Valentina Catania e, Flavio Nobili f, Dario Arnaldi f, Francesco Famà f, Francesco Orzi g, Carla Buttinelli g, Franco Giubilei g, Laura Bonanni h, Raffaella Franciotti h, Marco Onofrj h, Paola Stirpe b, Peter Fuhr i, Ute Gschwandtner i, Gerhard Ransmayr j, Lucia Fraioli b, Lucilla Parnetti k, Lucia Farotti k, Michela Pievani l, Fabrizia D’Antonio m, Carlo De Lena m, Bahar Güntekin n, Lutfu Hanoglu o, Görsev Yener p, Derya Durusu Emek-Savas¸ q, Antonio Ivano Triggiani r, John Paul Taylor s, Ian McKeith s, Fabrizio Stocchi b, Laura Vacca t, Giovanni B. Frisoni l,u, Maria Francesca De Pandis b a Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza”, Rome, Italy b Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy, and Hospital San Raffaele of Cassino, Italy c IRCCS SDN, Napoli, Italy d Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy e Oasi Research Institute - IRCCS, Troina, Italy f IRCCS Ospedale Policlinico San Martino, Italy - Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy g Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza”, Rome, Italy h Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d’Annunzio of Chieti-Pescara, Chieti, Italy i Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland j Department of Neurology 2, Med Campus III, Faculty of Medicine, Johannes Kepler University, Kepler University Hospital, Linz, Austria k Centre for Memory Disturbances, Lab of Clinical Neurochemistry, Section of Neurology, University of Perugia, Perugia, Italy l Laboratory of Alzheimer’s Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy m Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy n Department of Biophysic, I stanbul Medipol University, Istanbul, Turkey o Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey p Department of Neurosciences and Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey q Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey r Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy s Institute of Neuroscience, Newcastle University, Newcastle, UK t Casa di Cura Privata del Policlinico (CCPP) Milano SpA, Milan, Italy u Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland article info abstract

Article history: We hypothesized that dopamine neuromodulation might affect cortical excitability in Parkinson’s dis- Received 13 June 2018 Received in revised form 7 August 2018 ease (PD) patients set in quiet wakefulness, as revealed by resting state eyes-closed electroencephalo- e Accepted 8 August 2018 graphic (rsEEG) rhythms at alpha frequencies (8 12 Hz). Clinical and rsEEG rhythms in PD with dementia Available online 30 August 2018 (N ¼ 35), PD with mild cognitive impairment (N ¼ 50), PD with normal cognition (N ¼ 35), and normal (N ¼ 50) older adults were available from an international archive. Cortical rsEEG sources were estimated Keywords: by exact low-resolution brain electromagnetic tomography. Compared with the normal older group, the < Functional brain connectivity PD groups showed reduced occipital alpha sources and increased widespread delta ( 4 Hz) sources. Resting state EEG rhythms Widespread frontal and temporal alpha sources exhibited an increase in PD with dementia compared Mild cognitive impairment due to with PD with mild cognitive impairment and PD with normal cognition groups, as function of dopamine

* Corresponding author at: Department of Physiology and Pharmacology "V. Erspamer" University of Rome "La Sapienza", P. le A. Moro 5, Rome 00185, Italy. Tel.: þ39 0649910989; fax: þ39 0649910917. E-mail address: [email protected] (C. Babiloni).

Alzheimer’s disease (ADMCI) depletion severity, typically greater in the former than the latter groups. A daily dose of levodopa Mild cognitive impairment due to induced a widespread reduction in cortical delta and alpha sources in a subgroup of 13 PD patients under ’ Parkinson s disease (PDMCI) standard chronic dopaminergic regimen. In PD patients in quiet wakefulness, alpha cortical source activations may reﬂect an excitatory effect of dopamine neuromodulation. Ó 2018 Elsevier Inc. All rights reserved.

1. Introduction reflect cortical over-excitability in quiet wakefulness (Babiloni et al., 2015), but the relationship of this effect with dopamine neuro- Parkinson’s disease (PD) is characterized by motor symptoms modulation is poorly known. This is particularly pertinent, given but often goes hand in hand with subtle mild cognitive impairment PDCD are affected by neurodegenerative neuropathology of at the disease onset. This cognitive dysfunction is progressive in subcortical projection to cerebral cortex lobe including deposition most of patients (Muslimovic et al., 2005). Indeed, the percentage of of a-synuclein in Lewy bodies and neurites, loss of tegmental PD patients progressing to dementia in 20 years is included be- dopamine cell populations, loss of basal forebrain cholinergic pro- tween 60% (Aarsland et al., 2003; Buter et al., 2008; Hughes et al., jection to the cortex, and a variable degree of coexisting AD 2000; Levy et al., 2000) and more than 80% (Hely et al., 2008). neuropathology (Barker and Williams-Gray, 2016; Irwin et al., The pathoetiology of the cognitive deficits in PD is heteroge- 2017). neous and thus patients may need personalized treatments to The present exploratory and observational study tested the hy- mitigate the cognitive decline. Ideally, this monitoring should be pothesis that the dopamine neuromodulation may affect cortical done with biomarkers reflecting those neuropathological processes excitability in PD patients set in quiet wakefulness, as assessed by or the effects of those processes on brain functions. Among various the examination of resting state cortical alpha rhythms. Our aim candidates, resting state eyes-closed electroencephalographic therefore was to make use of clinical and rsEEG rhythms in PDD, (rsEEG) rhythms are cost-effective, noninvasive, and can be mild cognitive impairment due to Parkinson’s disease (PDMCI), PD repeated several times. For this reason, they have extensively been with normal cognition, and Nold subjects from an available inter- studied as biomarkers to evaluate the neurophysiological mecha- national archive including all data coming from the PDWAVE Con- nisms of dementing disorders (Breslau et al.,1989; Briel et al.,1999). sortium. An assumption of this study was that compared to PD A prominent application of quantitative rsEEG techniques in PD patients without cognitive deficits, those with MCI and dementia patients was the computation of power (density) of rsEEG rhythms have had a longer disease with a consequent progression in the in standard frequency bands from delta (<4 Hz) to gamma (>30 Hz) depletion of dopaminergic brain neurons, although the relationship at scalp electrodes placed over the whole scalp, as a neurophysio- between duration and severity of the disease is not linear. For some logical biomarker of thalamocortical and corticothalamic neural of these patients under a standard chronic dopaminergic regimen, synchronization of oscillatory cycles of cellular excitation and in- clinical and rsEEG rhythms were available before (OFF) and after hibition. Results have shown that when compared to normal (ON) about 60 minutes from the administration of 1 daily dose of healthy elderly (Nold) individuals, PD patients with cognitive def- levodopa. From a methodological point of view, we used exact low- icits (PDCD) were characterized by widespread higher power in resolution brain electromagnetic tomography (eLORETA) freeware delta and theta (4e7 Hz) rhythms and some reduction of alpha to estimate rsEEG cortical sources at individual frequency bands power (Bonanni et al., 2008; Bosboom et al., 2006, 2009; Caviness (Pascual-Marqui, 2007) to improve the topographic analysis of et al., 2016; Fünfgeld, 1995; Kamei et al., 2010; Melgari et al., alpha rhythms. In previous studies of our group, this methodology 2014; Pugnetti et al., 2010; Serizawa et al., 2008). PDCD also unveiled different characteristics of abnormal cortical delta and showed lower frequency of the alpha (8e12 Hz) power peak, alpha source activity in PDCD in relation not only to Nold subjects greater global theta and delta power, and lower power in the alpha but also to patients with cognitive deficits due to AD and DLB and beta (13e30 Hz) frequency bands even compared with a PD (Babiloni et al., 2017a,b, 2018a,b). Compared with Nold subjects, group with normal cognitive status (Caviness et al., 2007). PDCD showed a reduced activation in posterior cortical sources of The above rsEEG techniques have received some validation for alpha rhythms, associated with increased widespread delta source clinical applications in individual patients. A stepwise method us- activation. Furthermore, such increment in delta source activation ing an input of 20 discriminant rsEEG power and coherence for a was greater than that observed in AD and DLB patients. In contrast, statistical pattern recognition procedure exhibited an accuracy of PDCD patients exhibited less reduction in alpha source activation classification (area under the receiving operator characteristic than that observed in AD and DLB patients. Keeping in mind, these curve) of 0.90 between participants with dementia due to AD (ADD) previous data, the present methodology may provide quite sensi- and PD (PDD; Engedal et al., 2015). Another study using a multi- tive markers of cortical neural synchronization and excitability in modal combination of rsEEG variables with clinical, cerebrospinal PD patients as a function of cognitive deficits, which are a rough but fluid, neuroimaging, neuropsychological, and visual EEG data useful index of PD progression and dopamine depletion severity reached a classification accuracy of 0.87 in the discrimination be- over time. tween ADD, PDD, and dementia with Lewy bodies (DLB) individuals (Dauwan et al., 2016). Finally, a rsEEG study in a small database 2. Materials and methods obtained a 100% classification accuracy of individuals with PDD/ DLB, ADD, and frontotemporal dementia using 25 discriminant 2.1. Subjects and diagnostic criteria variables based on scalp rsEEG power and interrelatedness (i.e., Granger causality) as an input to a support vector machine (Garn In the present exploratory and observational study, clinical and et al., 2017). rsEEG data in age-, sex-, and education-matched groups of PD pa- In summary, compared with Nold individuals, PDCD show a tients and Nold subjects were taken from an international archive reduced activation of posterior cortical sources of alpha rhythms in formed by the Clinical Units of our Consortium. Specifically, the quiet wakefulness, which is associated with increased widespread groups were formed by 35 PDD, 50 PDMCI, 35 PD with normal delta rhythms. It is well known that such reduced alpha rhythms cognition, and 35 Nold individuals. Table 1 summarizes the most C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20 11

Table 1 Mean values (standard error of the mean) of the demographic and clinical data and results of their statistical comparisons (p < 0.05) in the present groups of healthy normal seniors, Parkinson’s disease subjects with normal cognition, with mild cognitive impairment, and dementia

Demographic characteristics and global cognitive status Nold PD PDMCI PDD Statistical analysis N50355035- Age (y) 70.8 (0.7 SE) 71.3 (1.2 SE) 71.5 (1.0 SE) 73.8 (1.2 SE) ANOVA: n.s. Gender (F/M) 26/24 18/17 29/21 21/14 Freeman-Halton: n.s. Education (y) 9.2 (0.4 SE) 8.9 (0.7 SE) 8.8 (0.6 SE) 7.9 (0.6 SE) ANOVA: n.s. MMSE score 28.4 (0.1 SE) 28.2 (0.3 SE) 25.6 (0.4 SE) 19.0 (0.8 SE) Kruskal-Wallis: H ¼ 94.1 p < 0.00001 (PDD < PDMCI < PD, Nold)

Key: SE, standard error of the mean; MMSE, Mini-Mental State Examination; M/F, males/females; n.s., not significant (p > 0.05); Nold, groups of healthy normal seniors; PD, Parkinson’s disease; PDMCI, Parkinson’s disease subjects with mild cognitive impairment; PDD, Parkinson’s disease subjects with dementia. relevant demographic features (i.e., age, gender, education) and The status of PDMCI was based on the diagnostic criteria for mild global cognitive status (i.e., Mini-Mental State Examination [MMSE] cognitive impairment in PD (Litvan et al., 2011). Specifically, the score) of those groups. Furthermore, the table reports the results of inclusion criteria comprised (1) a diagnosis of PD as specified pre- the presence or absence of statistically significant differences (p < viously; (2) a gradual decline, in the context of an established PD, in 0.05) between the groups as age (t-test), gender (Mann-Whitney U the cognitive status reported by either the patient or a reliable test), education (t-test), and MMSE score (Mann-Whitney U test). informant, or observed by the clinicians; (3) an abnormally low No statistically significant differences were found between the Nold score to at least 1 of the neuropsychological tests mentioned in a and the PD groups in those variables (p > 0.05) with the obvious following section, as defined by performances 1.5 standard de- exception of MMSE score. viations from the mean value for age- and education-matched In a subgroup of PD patients, eyes-closed (3e5 minutes) rsEEG controls or equivalent scores for abnormality according to relative data were recorded in the late morning before (OFF) and after (ON) manuals of the tests used; (4) at least a partial functional inde- about 60 minutes from the acute administration of 1 daily dose of pendence in instrumental activities of the daily living (ADL) under levodopa. In the ON condition, it is expected a peak of levodopa in the effect of dopamine treatment (ON condition), although slight the blood flow (Adamiak et al., 2010; Simon et al., 2016). Specif- difficulties on complex functional tasks may be present. Clinical ically, this subgroup was formed by 7 PD, 4 PDMCI, and 2 PDD dementia rating score of 0.0 or 0.5; and (5) daily chronic levodopa patients. Table 2 reports relevant demographic features (i.e., age, regimen for more than 6 months to ensure that the daily admin- gender, education) and, in ON and OFF conditions, the global istration of the dose of levodopa (875 mg/die 121 SE) in the cognitive status (i.e., MMSE score). present experimental day did not induce any placebo effect. This Local institutional Ethics Committee approved the present regimen included levodopa or melevodopa with other dopamine observational study. All experiments were performed with the agonists such as rotigotine, ropinirole, pramipexole, amantadine, informed and overt consent of each participant or caregiver, in line and monoamine oxidase inhibitor (selegiline). Catechol-O-methyl with the Code of Ethics of the World Medical Association (Decla- transferase inhibitor (i.e., entacapone) or DOPA decarboxylase in- ration of Helsinki) and the standards established by the local hibitor (i.e., carbidopa or benserazide) were also given in some Institutional Review Board. patients. In this study, the diagnosis of PD was based on a standard The exclusion criteria for PDMCI included the following forms of clinical assessment of tremor, rigidity, and bradykinesia (Gelb et al., parkinsonism: (1) dementia of any kind, including DLB (Geser et al., 1999). As measures of severity of motor disability, the Hoehn and 2005; McKeith et al., 1996, 2017), (2) drug-induced or cerebrovas- Yahr stage (Hoehn and Yahr, 1967) and the Unified Parkinson Dis- cular parkinsonism, (3) atypical parkinsonism with absent or ease Rating Scale-III (UPDRS-III; Fahn and Elton, 1987; Martínez- minimal responses to antiparkinsonian drugs, and (4) mixed Martín et al., 2015) for extrapyramidal symptoms were used. The neurodegenerative diseases as revealed by cerebrospinal fluid or mean UPDRS score was 15.1 (1.2 standard error of the mean, SE) in neuroimaging (i.e., structural magnetic resonance imaging, posi- the PD group, 21.0 (2.4 SE) in the PDMCI group, and 39.8 (3.5 SE) tron emission tomography, or single-photon computerized emis- in the PDD group. The ANOVA showed a significant effect (p < sion tomography). 0.0001) of the factor Group (PD, PDMCI, and PDD) with the mean A diagnosis of PDD was given to the patients with a history of UPDRS score higher in the PDD than the PDMCI and the PD groups dementia preceded by a diagnosis of PD for at least 12 months. On (p< 0.05). the basis of the clinical features and neuroradiological findings, the inclusion criteria included (1) a diagnosis of PD as specified previously; (2) a gradual decline, in the context of an established PD, in Table 2 Mean values (SE) of the demographic and clinical data as well as the results of their the cognitive status reported by either the patient or a reliable statistical comparisons (p < 0.05) in a small subgroup of 13 PD patients (e.g., 7 PD, 4 informant, or observed by the clinicians; (3) an abnormally low PDMCI, and 2 PDD individuals) score to at least 1 of the neuropsychological tests mentioned in a fi Demographic characteristics and global cognitive status following section, as de ned by performances 1.5 standard de- viations from the mean value for age- and education-matched N13 Cognitive status 7 PD, 4 PDMCI, 2 PDD controls or equivalent scores for abnormality according relative Age (y) 67.1 (2.1 SE) manuals of the tests used; (4) moderate-to-severe impairment in Gender (F/M) 4/7 the functional independence in instrumental ADL under the effect Education (y) 8.0 ( 0.9 SE) of dopamine treatment (ON condition). Clinical dementia rating MMSE score (OFF) 26.8 (1.1 SE) MMSE score (ON) 26.6 (1.0 SE) score greater than 0.05; and (5) daily chronic levodopa regimen (see above the list of the drugs) for more than 6 months to ensure Key: SE, standard error of the mean; MMSE, Mini-Mental State Examination; M/F, that the daily administration of the dose of levodopa in the present males/females; n.s., not significant (p > 0.05); PD, Parkinson’s disease; PDMCI, Parkinson’s disease subjects with mild cognitive impairment; PDD, Parkinson’s experimental day did not induce any placebo effect. The exclusion disease subjects with dementia. criteria for PDD included the following forms of parkinsonism: (1) 12 C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20

DLB (McKeith et al., 1996), (2) drug-induced parkinsonism, (3) ce- 2.3. Spectral analysis of the rsEEG epochs rebrovascular parkinsonism, and (4) atypical parkinsonism with absent or minimal responses to antiparkinsonian drugs. A standard digital Fast Fourier Transform-based power spectrum All PD patients underwent a battery of clinical scales including analysis (Welch technique, Hanning windowing function, no phase neuropsychiatry inventory (NPI; Cummings et al. (1994), Epworth shift) computed the power density of scalp rsEEG rhythms with Sleepiness Scale (Johns, 1991), geriatric depression scale (GDS; 0.5 Hz of frequency resolution. Yesavage et al., 1983), ADL, and the Barthel index for the evalua- According to a previous study of our group (Moretti et al., 2004), tion of independence in the daily activities. Cognitive functions the frequency bands of interest were individually identified based were tested by neuropsychological tests including Montreal on the following frequency landmarks: the transition frequency Cognitive Assessment (Nasreddine et al., 2005), MMSE (Folstein (TF) and the individual alpha frequency peak (IAF). In the EEG po- et al., 1975), and Frontal Assessment Battery (FAB; Dubois et al., wer density spectrum, the TF marks the transition frequency be- 2000). tween the theta and alpha bands, defined as the minimum of the Concerning psychoactive medications add-on to levodopa, some rsEEG power density between 3 and 8 Hz (between the delta and PD patients used antidepressants (i.e., selective serotonin reuptake the alpha power peak). IAF is defined as the maximum power inhibitor: sertraline, citalopram, paroxetine, escitalopram, or density peak between 6 and 14 Hz. These frequency landmarks acetylcholinesterase inhibitors, AChEIs (i.e., rivastigmine). Less than were previously well described by Dr. Wolfgang Klimesch 10% of the PD patients took antipsychotic medications (i.e., cloza- (Klimesch, 1996, 1999; Klimesch et al., 1998). pine, quetiapine). TF and IAF were individually computed for each subject involved All Nold subjects underwent a cognitive screening (including in the study. Based on TF and IAF, we estimated the frequency band MMSE and GDS) as well as physical and neurological examinations range for each subject as follows: delta from TF -4 Hz to TF -2 Hz, to exclude any dementia or major cognitive deficit or psychiatric theta from TF -2 Hz to TF, low-frequency alpha band (alpha 1 and disorder. All Nold subjects had a GDS score lower than the threshold alpha 2) from TF to IAF, and high-frequency alpha band (or alpha 3) of 5 (no depression) or no depression after an interview with a from IAF to IAFþ2 Hz. The other bands were defined based on physician or clinical psychologist. Subjects affected by chronic sys- standard fixed frequency ranges: beta 1 from 14 to 20 Hz, beta 2 temic illnesses (diabetes mellitus, as an example) were excluded, as from 20 to 30 Hz, and gamma from 30 to 40 Hz. The alpha 1 and well as subjects receiving chronic psychoactive drugs. Subjects with alpha 2 bands were computed for each subject as follows: alpha 1 a history of previous or present neurological or psychiatric disease from TF to the midpoint of the TF-IAF range and alpha 2 from this were also excluded. midpoint to IAF.

2.2. rsEEG recordings and preliminary data analysis 2.4. Cortical sources of rsEEG epochs as computed by eLORETA

Before the EEG recording, instructions encouraged the Nold and We used the freeware tool called eLORETA for the linear esti- PDCD individuals to experience a quiet wakefulness with muscle mation of the cortical source activity generating scalp-recorded relaxation, no voluntary movements, no talking, or developing a rsEEG rhythms (Pascual-Marqui, 2007). The present implementa- systematic goal-oriented mentalization. A quiet wondering mode of tion of eLORETA uses a head volume conductor model composed of mentalization was kindly required. the scalp, skull, and brain. In the scalp compartment, exploring The rsEEG data were recorded with a sampling frequency of electrodes can be virtually positioned to give EEG data as an input to 256-1024 Hz and related antialiasing bandpass between 0.01 Hz the source estimation (Pascual-Marqui, 2007). The brain model is and 100 Hz. Nineteen scalp electrodes positioned according to the based on a realistic cerebral shape taken from a template typically 10e20 System (i.e., Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, used in the neuroimaging studies, namely that of the Montreal P3, Pz, P4, T6, O1, and O2) were used with respect to a cephalic Neurological Institute (MNI152 template). The eLORETA freeware ground (i.e., frontal) and an extracephalic electrode reference (i.e., solves the so-called EEG inverse problem estimating “neural” cur- linked auricular or mastoid). Electrode impedances were kept rent density values at any cortical voxel of the mentioned head below 5 KOhm. Bipolar vertical and horizontal electrooculographic volume conductor model. The solutions are computed rsEEG fre- and one-channel electrocardiographic signals were also acquired quency bin-by-frequency bin. (256-1024 Hz) for artifact detection and off-line correction (when The input for this estimation is the EEG spectral power density possible). computed at 19 scalp electrodes. The output is the electrical brain The recorded rsEEG data were divided into epochs of 2 seconds source space formed by 6239 voxels with 5 mm resolution, and analyzed off-line. The epochs affected by any physiological restricted to cortical gray matter of the head volume conductor (ocular/blinking, muscular, head movements) or nonphysiological model. An equivalent current dipole is located in each voxel. For (bad contact electrode-scalp) artifacts were preliminarily identified each voxel, the eLORETA package provides the Talairach co- by an automatic computerized procedure (Moretti et al., 2003). ordinates, the lobe, and the Brodmann area. Furthermore, 2 independent experimenters manually checked and In line with the general low spatial resolution of the present EEG (dis)confirmed the artifact-free rsEEG epochs, before successive methodological approach (i.e., 19 scalp electrodes), we performed a analyses. Specifically, they controlled for the presence of ocular and regional analysis of the eLORETA solutions. For this purpose, we blinking artifacts based on electrooculographic signals, whereas collapsed the eLORETA solutions within frontal, central, temporal, muscular and head artifacts were recognized by analyzing EEG parietal, occipital, and limbic macroregions (regions of interest signals. Moreover, head artifacts were detected by a sudden and [ROIs]) considered separately. Table 3 reports the list of the Brod- great increase in amplitude of slow EEG waves in all scalp elec- mann areas used for the ROIs considered in the present study. Of trodes. Finally, muscle artifacts were recognized observing the ef- note, the main advantage of the regional analysis of eLORETA so- fects of several frequency bandpass filters in different ranges and by lutions was that we could disentangle the rsEEG source activity in the inspection of EEG power density spectra. Indeed, muscle ten- contiguous cortical areas. For example, the rsEEG source activity in sion is related to unusually high and stable values of EEG power the occipital ROI was disentangled from that estimated in the pa- density from 30 to 100e150 Hz, which contrast with the typical rietal and temporal ROIs, etc. This was made possible by the fact declining trend of EEG power density from 25 Hz onward. that eLORETA solves the linear inverse problem by taking into C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20 13

Table 3 Condition and Band (p < 0.05), (2) a post hoc test indicating sta- Broadmann areas corresponding to the regions of interest in solutions of the exact tistically significant differences of the regional normalized eLORETA low-resolution brain electromagnetic tomography freeware to estimate rsEEG solutions in the alpha frequency band with the pattern ON s OFF cortical sources (Duncan test, p < 0.05). Brodmann areas into the regions of interest Frontal 8, 9, 10, 11, 44, 45, 46, 47 Central 1, 2, 3, 4, 6 2.6. Correlation between the clinical scale and the eLORETA Parietal 5, 7, 30, 39, 40, 43 solutions in the ON condition Temporal 20, 21, 22, 37, 38, 41, 42 Occipital 17, 18, 19 Spearman test (p < 0.05) was used to evaluate the correlation Limbic 31, 32, 33, 34, 35, 36 between the MMSE score and rsEEG source activities showing Key: rsEEG, resting state eyes-closed electroencephalographic. statistically significant differences (p < 0.05) between the PD groups (i.e., PD, PDMCI, and PDD). Indeed, a reduction in wide- account (at least in part) the effects of the head as a volume spread rsEEG cortical source activation in the alpha range as a fi conductor. In contrast, the solutions of rsEEG power density function of cognitive de cits may be interpreted as a clinically computed at a parietal scalp electrode reflect the contribution of relevant increase in the resting state cortical excitability, possibly source activities not only of the underlying parietal cortex but also related to the dopamine depletion. In the same line, Spearman test < of surrounding occipital and temporal cortices. (p 0.05) was also used to evaluate the correlation between the For the present eLORETA cortical source estimation, a frequency MMSE score and rsEEG source activities showing statistically sig- fi < resolution of 0.5 Hz was used, namely, the maximum frequency ni cant differences (p 0.05) between the ON and OFF conditions resolution allowed by the use of 2-s artifact-free EEG epochs. in the subgroup of PD patients examined.

2.6.1. Accuracy of the rsEEG source activity in the discrimination 2.5. Statistical analysis of the rsEEG eLORETA source activity between Nold, PD, PDMCI and PDD individuals rsEEG sources showing the highest statistically significant dif- All the statistical sessions were performed by the commercial ferences between the 4 groups were used as discriminant variables tool STATISTICA 10 (StatSoft Inc., www.statsoft.com) to test 2 main for the classification of Nold and PD individuals of a given group and hypotheses. First, alpha source activities might differ in PD groups between the individuals of 2 groups of PD patients. The correct (i.e., PD, PDMCI, PDD) as a function of the severity of the cognitive blind classifications of these rsEEG source activities were per- deficits (i.e., as a probe of dopamine depletion severity). Second, formed by MATLAB 2010b software (Mathworks Inc., Natick, MA, rsEEG source activities showing that effect in PD patients with USA) for the production of the receiver operating characteristic different cognitive deficits might show a modulation between the (ROC) curves (DeLong et al., 1988). The following indexes measured ON and OFF conditions in those patients, thus confirming its de- the performance of the above binary classification: (1) sensitivity: pendency by brain dopamine levels. In all the ANOVAs, Mauchly’s the rate of positives who were correctly classified as positives (i.e., test evaluated the sphericity assumption, and degrees of freedom “true positive rate” in the signal detection theory); (2) specificity: were corrected by the Greenhouse-Geisser procedure when the rate of negatives (control) who were correctly classified as appropriate (p < 0.05). Duncan test was used for post hoc com- negatives (i.e., “true negative rate” in the signal detection theory); parisons (p < 0.05). The results of the following statistical analyses (3) accuracy: the mean of the sensitivity and specificity (the amount were controlled by the Grubbs test (p < 0.01) for the presence of of subjects in the groups was the same); and (4) area under the ROC outliers in the distribution of the eLORETA source solutions. (AUROC) curve. The AUROC curve was a major reference index of A first ANOVA was computed using the regional normalized the global classification accuracy. eLORETA solutions (normalized current density at all voxels of a given ROI) as a dependent variable (p < 0.05). The ANOVA factors were Group (Nold, PD, PDMCI, PDD), Band (delta, theta, alpha 1, 3. Results alpha 2, alpha 3, beta 1, beta 2, and gamma), and ROI (frontal, central, parietal, occipital, temporal, and limbic). Individual TF and 3.1. Statistical comparison of the EEG cortical sources in the Nold, the IAF were used as covariates. The sphericity assumption was PD, PDMCI, and PDD groups evaluated with the Mauchly’s test. The degrees of freedom were corrected by the means of Greenhouse-Geisser procedure when Table 4 reports the mean values of TF and IAF for the 4 groups appropriate. The Duncan test was used for post hoc comparisons (p (i.e., Nold, PD, PDMCI, and PDD), together with the results of the < 0.05). The planned post hoc testing evaluated the prediction of statistical comparisons between the groups (ANOVA, p < 0.05). The the differences in rsEEG source solutions related to cognitive defi- mean TF was 6.2 Hz (0.1 SE) in the Nold group, 5.8 Hz (0.2 SE) in cits between the PDMCI and PDD groups using the Nold and PD the PD group, 5.5 Hz (0.2 SE) in the PDMCI group, and 4.7 Hz (0.2 groups as control references. Specifically, we expected (1) a statis- SE) in the PDD group. Furthermore, the mean IAF was 9.5 Hz (0.1 tical 3-way interaction effect including the factors Group, ROI, and SE) in the Nold group, 8.6 Hz (0.2 SE) in the PD group, 8.5 Hz (0.2 Band (p < 0.05), (2) a post hoc test indicating statistically significant SE) in the PDMCI group, and 7.3 Hz (0.2 SE) in the PDD group. The differences of the regional normalized eLORETA solutions with the ANOVAs using these values as an input showed the following sta- pattern Nold and PD s PDMCI and PDD (Duncan test, p < 0.05). tistically significant findings: (1) a significant effect (F ¼ 12.3, p < A second ANOVA was computed using the regional normalized 0.00001) of the factor Group (Nold, PD, PDMCI, and PDD) with the eLORETA solutions as a dependent variable (p < 0.05). The ANOVA mean TF lower in the PDD than PDMCI, PD, and Nold (p< 0.05). The factors were Condition (ON, OFF), Band (delta, theta, alpha 1, alpha mean TF was also lower in the PDMCI than the Nold group (p< 2, alpha 3, beta 1, beta 2, and gamma), and ROI (frontal, central, 0.05), (2) a significant effect (F ¼ 24.6, p < 0.0001) of the factor parietal, occipital, temporal, and limbic). The planned post hoc Group (Nold, PD, PDMCI, and PDD) with the mean IAF lower in the testing evaluated the prediction of the differences in rsEEG source PDD than the PDMCI and the PD group (p< 0.05). The mean IAF was solutions between the ON and OFF conditions. Specifically, we ex- also lower in the PDMCI and PD groups than the Nold group (p< pected: (1) a statistical interaction effect including the factor 0.05). 14 C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20

Table 4 Mean values (SE) of transition frequency and individual alpha frequency peak computed from resting state eyes-closed electroencephalographic power density spectra in the groups of Nold, PD, PDMCI, and PDD subjects

Mean values (SE) of transition frequency and individual alpha frequency peak Nold PD PDMCI PDD Statistical analysis TF 6.2 (0.1 SE) 5.8 (0.2 SE) 5.5 (0.2 SE) 4.7 (0.2 SE) ANOVA: F ¼ 12.3 p < 0.00001 (PDD < PDMCI, PD, Nold; PDMCI < Nold) IAF 9.5 (0.1 SE) 8.6 (0.2 SE) 8.5 (0.2 SE) 7.3 (0.2 SE) ANOVA: F ¼ 24.6 p < 0.0001 (PDD < PDMCI, PD < Nold)

Key: IAF, individual alpha frequency peak; Nold, groups of healthy normal seniors; PD, Parkinson’s disease; PDMCI, Parkinson’s disease subjects with mild cognitive impairment; PDD, Parkinson’s disease subjects with dementia. The table also reports the p values of the statistical comparisons of these values between the groups (p < 0.05).

These findings stress the importance of individual differences in activities (p < 0.05 to p < 0.000001). As additional findings of in- the TF and IAF in the determination of the delta to alpha frequency terest, alpha 3 sources showed a paradoxical greater magnitude as a bands in PD patients. function of the global cognitive deficits. Specifically, a pattern “PD < Figure 1 shows the grand average of regional rsEEG source ac- PDMCI < PDD” was fitted by temporal alpha 3 source activities (p < tivities (i.e., eLORETA solutions) relative to a statistically significant 0.05 to p < 0.000001). Furthermore, a pattern “PD and PDMCI < ANOVA interaction effect (F ¼ 5.3; p < 0.00001) among the factors PDD” was fitted by frontal, central, and temporal alpha 3 source Group (Nold, PD, PDMCI, and PDD), Band (delta, theta, alpha 1, alpha activities (p < 0.05 to p < 0.000001). Of note, these findings were not 2, alpha 3, beta 1, beta 2, gamma), and ROI (frontal, central, parietal, due to outliers from those individual eLORETA solutions, as shown occipital, temporal, limbic). In the figure, rsEEG source activities have by Grubbs’ test with an arbitrary threshold of p < 0.0001 (Fig. 2). the shape of typical rsEEG relative power spectra. Notably, the profile The above effects in the delta sources were clinically relevant as and magnitude of the spectra of the rsEEG source activity in the Nold, shown by results of a correlation analysis between those source PD, PDMCI, and PDD groups are different across the ROIs, thus sug- activities and the MMSE score in the Nold, PD, PDMCI, and PDD gesting the concept that scalp-recorded rsEEG rhythms are pro- subjects as a whole group (Spearman test, p < 0.05). Specifically, a duced by a distinct pattern of cortical source activity in these groups. statistically significant negative correlation was found between Concerning the most relevant effects, the discriminant pattern widespread delta source activities and the MMSE score (r ¼0.32 Nold and PD < PDMCI and PDD was fitted by the frontal, central, to 0.48, p < 0.0001 to 0.000001; Table 5). The higher the delta parietal, temporal, occipital, and limbic delta source activities (p < source activities, the lower the MMSE score (the worse the global 0.05 to p < 0.000001). Furthermore, the discriminant pattern Nold cognition). These effects were also confirmed when all PD, PDMCI, and PD > PDMCI and PDD was fitted by occipital alpha 2 source and PDD patients were considered as a whole group (Spearman

Fig. 1. Regional normalized exact low-resolution brain electromagnetic tomography (eLORETA) solutions (mean across subjects) of the rsEEG rhythms relative to a statistical ANOVA interaction among the factors Group [normal healthy elderly (Nold) subjects; Parkinson’s disease subjects with normal cognition (PD); PD subjects with mild cognitive impairment (PDMCI); PD subjects with dementia (PDD)], Band (delta, theta, alpha 1, alpha 2, alpha 3, beta 1, beta 2 and gamma), and region of interest (ROI) (central, frontal, parietal, occipital, temporal, limbic). This ANOVA design used the regional normalized eLORETA solutions as a dependent variable. Regional normalized eLORETA solutions modeled the rsEEG relative power spectra as revealed by a sort of “virtual” intracranial macroelectrodes located on the macrocortical regions of interest. The rectangles indicate the cortical regions and frequency bands in which the eLORETA solutions statistically presented signiﬁcant eLORETA patterns “PD s PDMCI s PDD” (p < 0.05). Abbreviation: rsEEG, resting state eyes- closed electroencephalographic. C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20 15

Fig. 2. Individual values of the normalized eLORETA solutions from delta to gamma in Nold, PD, PDMCI, and PDD individuals. Grubbs’ test showed no outlier at the arbitrary threshold of p < 0.0001. Abbreviations: eLORETA, exact low-resolution brain electromagnetic tomography; Nold, healthy normal seniors; PD, Parkinson’s disease subjects with normal cognition; PDD, PD subjects with dementia; PDMCI; PD subjects with mild cognitive impairment. test, p < 0.05). Indeed, a statistically significant negative correlation curves. These AUROC curves aimed at indexing the classification was found between widespread delta source activities and the accuracy in the discrimination between Nold, PD, PDMCI, and PDD MMSE score (r ¼0.24 to 0.42, p < 0.005 to 0.00005; Table 5). Of individuals. The results of this analysis are reported in detail in note, alpha 3 sources showing a paradoxical greater magnitude as a Table 6. In general, the best classification accuracies between Nold function of the global cognitive deficits showed no statistically versus PDCD (PDMCI, PDD) were obtained using widespread delta significant correlation with the MMSE score in the Nold, PD, PDMCI, source activities. AUROC values were of 0.73e0.81 for the Nold and PDD subjects as a whole group (Spearman test, p > 0.05). versus PDMCI individuals and 0.90e0.95 for Nold versus PDD individuals. In the same line, the best classification accuracies between PD versus PDMCI and PDD subjects were still obtained using 3.2. Classification among Nold, PD, PDMCI, and PDD individuals widespread delta source activities. Specifically, AUROC values were based on delta and alpha source activities of 0.70e0.73 for the PD versus PDMCI individuals and 0.76e0.94 for PD versus PDD individuals. Finally, significant classification accu- The above significant delta and alpha source activities served as racies using “paradoxical” alpha sources (i.e., those showing greater discriminant input variables for the computation of the AUROC magnitude as a function of the global cognitive deficits) were as follows. AUROC values were of 0.74 for the Nold versus PD in- Table 5 dividuals and 0.76 for PD versus PDD individuals. Results of the correlation analysis (Spearman test, p < 0.05) performed between clinical scales and rsEEG cortical sources at delta and alpha sources in all Nold, PD, PDMCI, and PDD subjects considered as a whole group as well as all PD, PDMCI, and 3.3. Effect on delta and alpha sources of the administration of a PDD subjects considered as a whole group daily dose of levodopa in PD patients under chronic dopamine treatment Correlation between the eLORETA source activity of the rsEEG rhythms and the MMSE score Spearman R p Figure 3 shows the grand average of regional rsEEG source activities (i.e., eLORETA solutions) relative to a statistically significant Nold, PD, PDMCI, Frontal delta vs. MMSE 0.32 0.001 ¼ < and PDD subjects Central delta vs. MMSE 0.46 0.000001 ANOVA interaction effect (F 1.5; p 0.05) among the factors as a whole group Parietal delta vs. MMSE 0.48 0.000001 Condition (ON, OFF), Band (delta, theta, alpha 1, alpha 2, alpha 3, Occipital delta vs. MMSE 0.44 0.000001 beta 1, beta 2, gamma), and ROI (frontal, central, parietal, occipital, Temporal delta vs. MMSE 0.44 0.000001 temporal, limbic) in the small subgroup of 13 PD patients under Limbic delta vs. MMSE 0.41 0.000001 Temporal alpha 3 vs. MMSE n.s. n.s. chronic dopamine treatment (e.g., 7 PD, 4 PDMCI, and 2 PDD in- PD, PDMCI, and Frontal delta vs. MMSE 0.24 0.008 dividuals) whose rsEEG sources were compared before versus after PDD subjects Central delta vs. MMSE 0.42 0.000003 a daily dose of levodopa. In the figure, rsEEG source activities have as a whole group Parietal delta vs. MMSE 0.42 0.000002 the general shape of those of Figure 1, based on the eLORETA so- Occipital delta vs. MMSE 0.40 0.000009 lutions of the extended groups of PD, PDMCI, and PDD patients. Temporal delta vs. MMSE 0.38 0.00004 Limbic delta vs. MMSE 0.35 0.0001 When compared to the OFF condition, the ON condition induced a Temporal alpha 3 vs. MMSE n.s. n.s. marked reduction in parietal, temporal, and occipital alpha 2 and < < Key: eLORETA, exact low-resolution brain electromagnetic tomography; MMSE, alpha 3 source activities (p 0.05 to p 0.00001). Furthermore, the Mini-Mental State Examination; Nold, groups of healthy normal seniors; PD, Par- ON condition also induced a reduction in parietal and occipital delta kinson’s disease; PDMCI, Parkinson’s disease subjects with mild cognitive impair- source activities (p < 0.05 to p < 0.00001). As the only exception, ment; PDD, Parkinson’s disease subjects with dementia; rsEEG, resting state eyes- frontal delta source activities were higher in the ON than the OFF closed electroencephalographic. condition (p < 0.00001). Of note, these findings were not due to In particular, these results include Pearson’s correlation coefficient (r) and the associated level of significance (p). outliers from those individual eLORETA solutions, as shown by Bold indicates the statistically significant results (p < 0.05). Grubbs’ test with an arbitrary threshold of p < 0.0001 (Fig. 4). 16 C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20

Table 6 Results of the classiﬁcation among Nold, PD, PDMCI, and PDD individuals based on delta and alpha source activities

Classiﬁcation of Nold, PD, PDMCI, and PDD individuals based on rsEEG source activities (AUROC >0.7) Nold vs. PD Nold vs. PDMCI Nold vs. PDD PD vs. PDMCI PD vs. PDD PDMCI vs. PDD Frontal delta 0.903 0.851 0.715 Central delta 0.814 0.953 0.732 0.908 0.755 Parietal delta 0.800 0.945 0.723 0.898 0.753 Occipital delta 0.726 0.915 0.884 0.787 Temporal delta 0.770 0.941 0.731 0.941 0.742 Limbic delta 0.745 0.929 0.702 0.901 0.762 Temporal alpha 3 0.741 0.767

Key: eLORETA, exact low-resolution brain electromagnetic tomography; Nold, groups of healthy normal seniors; PD, Parkinson’s disease; PDMCI, Parkinson’s disease subjects with mild cognitive impairment; PDD, Parkinson’s disease subjects with dementia; rsEEG, resting state eyes-closed electroencephalographic. The classification rate is computed by the analysis of the area under the receiver operating characteristic (AUROC) curve. The table reports the classification accuracy for all the rsEEG source activities having a value higher than 0.70 in the AUROC curves. These source activities were those showing statistically significant differences between the groups in the main statistical analysis (i.e., Nold, PD, PDMCI, and PDD).

Table 7 reports results of the correlation analysis (Spearman test, unveiled the paradox that in those PD patients in the ON condition, p < 0.05) performed between clinical and neuropsychological the higher alpha 2 and alpha 3 source activities in some posterior scales and rsEEG cortical sources at delta and alpha sources in the areas, the greater the abnormalities in clinical and neuropsycho- small subgroup of 13 PD patients (e.g., 7 PD, 4 PDMCI, and 2 PDD logical tests (p < 0.05). individuals) in the ON condition (about 60 minutes after the administration of a daily dose of levodopa). A statistically signifi- 4. Discussion cant positive correlation was found between parietal alpha 2 source activities and the NPI score (r ¼ 0.56, p < 0.05) and between parietal In the present exploratory and observational study, we assessed alpha 3 source activities and GDS (r ¼ 0.61, p < 0.05). The higher the whether dopamine modulation may reflect cortical excitability in alpha source activities, the higher the NPI (the worse the psychiatric PD patients. When compared to the Nold group, the PD groups symptoms) and GDS (the worse the depression) scores. Further- without and with cognitive deficits (i.e., PDMCI and PDD) showed more, a statistically significant positive correlation was also found reduced activity in posterior alpha sources, which was associated between temporal alpha 2 (r ¼0.70, p < 0.005) and alpha 3 (r ¼ with increased activity in widespread delta (<4 Hz) sources. In 0.57, p < 0.05) source activities and the FAB score (r ¼ 0.56, p < relation to the PD group, the PDMCI and PDD groups showed a 0.05). The higher alpha source activities, the lower the FAB score greater reduction in occipital low-frequency alpha source activity, (the worsen the frontal executive dysfunctions). These results associated with greater increase in widespread delta (<4 Hz) source

Fig. 3. Regional normalized eLORETA solutions (mean across subjects) of the rsEEG rhythms before (OFF) and after (ON) about 60 minutes from the administration of 1 daily dose of levodopa in a small subgroup of 13 PD patients (e.g., 7 PD, 4 PDMCI, and 2 PDD individuals). These solutions are relative to a statistical ANOVA interaction (p < 0.05) among the factors Condition (OFF, ON), Band (delta, theta, alpha 1, alpha 2, alpha 3, beta 1, beta 2 and gamma), and ROI (central, frontal, parietal, occipital, temporal, limbic). The rectangles indicate the cortical regions and frequency bands in which the eLORETA solutions statistically presented a signiﬁcant eLORETA pattern “OFF s ON” (p < 0.05). Abbreviations: eLORETA, exact low-resolution brain electromagnetic tomography; Nold, healthy normal seniors; PD, Parkinson’s disease subjects with normal cognition; PDD, PD subjects with dementia; PDMCI, PD subjects with mild cognitive impairment. C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20 17

Fig. 4. Individual values of the normalized eLORETA solutions from delta to gamma in the small subgroup of 13 PD patients (e.g., 7 PD, 4 PDMCI, and 2 PDD individuals) for the OFF and ON conditions. Grubbs’ test showed no outlier at the arbitrary threshold of p < 0.0001. Abbreviations: eLORETA, exact low-resolution brain electromagnetic tomography; Nold, healthy normal seniors; PD, Parkinson’s disease subjects with normal cognition; PDD, PD subjects with dementia; PDMCI, PD subjects with mild cognitive impairment. activities. However, interestingly, widespread frontal, central, and the working hypothesis that dopamine neuromodulation in the temporal high-frequency alpha sources exhibited a paradoxical cerebral cortex may increase local neural pathological excitation in increase in activity in the PDD group compared with the PDMCI and quiet wakefulness. In line with this tentative explanation, the other PD groups. At the individual level, all delta sources exhibited clin- main results of this study showed that a daily dose of levodopa ically relevant correlations with global cognitive status (i.e., MMSE induced a widespread reduction in cortical delta and alpha source score) in all Nold and PD subjects considered as a whole group, activations in a subgroup of PD patients taking a standard chronic while some delta and alpha sources showed moderate (>0.7) to dopaminergic regimen. good (>0.9) classiﬁcation accuracies in the discrimination between The present results in the cortical rsEEG source space extend Nold and PDCD. previous evidence based on rsEEG and its magnetoencephalo- Two main assumptions underpin our interpretation of the pre- graphic (rsMEG) counterpart in PD patients compared with Nold sent ﬁndings. First, compared with PD, PDD is supposed to be subjects, namely a reduction in alpha and a decrease in delta power characterized by a longer neurodegenerative disease duration and at the scalp electrode level (Bonanni et al., 2008; Bosboom et al., consequently a greater loss of brain dopaminergic neurons, 2009, 2006; Caviness et al., 2007, 2016; Fünfgeld, 1995; Kamei although the relationship between duration and severity of the et al., 2010; Melgari et al., 2014; Pugnetti et al., 2010; Serizawa disease is not linear. Second, the resting state eyes-closed alpha et al., 2008). In this framework, rsMEG investigations showed a power in posterior scalp regions is typically lower in amplitude diffuse increase in delta power in PDD patients in comparison with when underlying cortical neurons are excited. Keeping in mind nondemented ones (Bosboom et al., 2006). Furthermore, those in- these assumptions, the results of the present study lends support to vestigations showed parieto-occipital and temporal alpha power

Table 7 Results of the correlation analysis (Spearman test, p < 0.05) performed between clinical and neuropsychological scales and rsEEG cortical sources at delta and alpha sources in the small subgroup of 13 PD patients (e.g., 7 PD, 4 PDMCI, and 2 PDD individuals) in the ON condition (about 60 min after the administration of a daily dose of levodopa)

Correlation between rsEEG sources vs. clinical and neuropsychological scores in PD patients in the ON condition Parietal delta Occipital delta Parietal alpha 2 Occipital alpha 2 Temporal alpha 2 Parietal alpha 3 Occipital alpha 3 Temporal alpha 3 H&Y on 0.2304 0.3054 0.3872 0.4342 0.5362 0.2427 0.2064 0.4534 p ¼ 0.449 p ¼ 0.310 p ¼ 0.191 p ¼ 0.138 p ¼ 0.059 p ¼ 0.424 p ¼ 0.499 p ¼ 0.120 UPDRS on 0.1097 0.0294 0.0196 0.1528 0.0658 0.0075 0.1376 0.0286 p ¼ 0.721 p ¼ 0.924 p ¼ 0.949 p ¼ 0.618 p ¼ 0.831 p ¼ 0.981 p ¼ 0.654 p ¼ 0.926 NPI on 0.1963 0.0669 0.5401 0.3850 0.2639 0.5579 0.2960 0.2772 p ¼ 0.520 p ¼ 0.828 p ¼ 0.057 p ¼ 0.194 p ¼ 0.384 p [ 0.048 p ¼ 0.326 p ¼ 0.359 MOCA on 0.0352 0.2118 0.1699 0.3421 0.4854 0.1168 0.2985 0.4225 p ¼ 0.909 p ¼ 0.487 p ¼ 0.579 p ¼ 0.253 p ¼ 0.093 p ¼ 0.704 p ¼ 0.322 p ¼ 0.150 MMSE on 0.0499 0.2652 0.3288 0.4426 0.4523 0.2170 0.3997 0.4122 p ¼ 0.871 p ¼ 0.381 p ¼ 0.273 p ¼ 0.130 p ¼ 0.121 p ¼ 0.476 p ¼ 0.176 p ¼ 0.162 FAB on 0.0710 0.1425 0.2473 0.3386 0.6984 0.0754 0.1474 0.5693 p ¼ 0.818 p ¼ 0.642 p ¼ 0.415 p ¼ 0.258 p [ 0.008 p ¼ 0.807 p ¼ 0.631 p [ 0.042 ESS on 0.2884 0.1509 0.4387 0.2889 0.3943 0.4553 0.3755 0.4342 p ¼ 0.339 p ¼ 0.623 p ¼ 0.134 p ¼ 0.338 p ¼ 0.182 p ¼ 0.118 p ¼ 0.206 p ¼ 0.138 GDS on 0.0304 0.0788 0.6122 0.3814 0.2742 0.5031 0.2145 0.2733 p ¼ 0.921 p ¼ 0.798 p [ 0.026 p ¼ 0.198 p ¼ 0.365 p ¼ 0.080 p ¼ 0.482 p ¼ 0.366

In particular, these results include Spearman correlation coefficient (r) and the associated level of significance (p). Bold indicates the statistically significant results (p < 0.05). The pathological cutoff of the clinical and neuropsychological scales are the following. Hoehn and Yahr stage (H&Y): cutoff 4 (range 0-5; 1-3 moderate); Unified Parkinson Disease Rating Scale (UPDRS-III Motor Part): cutoff 59 (range 0-80; 0-32 mild; 33-58 moderate); neuropsychiatory inventory (NPI): cutoff > 50 (range 0-144; 20 mild; 21-50 moderate); Montreal Cognitive Assessment (MOCA): cutoff 23 (range 0-30; 24-26 mild); Mini-Mental State Examination (MMSE): cutoff 20 (range 0-30; 21-24 mild); Frontal Assessment Battery (FAB): cutoff 14 (range 0-18); Epworth Sleepiness Scale (ESS): cutoff 16 (10-15 moderate); geriatric depression scale (GDS): cutoff 17 (0-10 depression absent; 11-16 moderate). 18 C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20 increased and frontocentral and parieto-occipital delta power rhythms recorded in the cerebral cortex and internal global pallidus decreased after an add-on regimen with acetylcholine inhibitors (Costa et al., 2006). This effect was paralleled by a decrease in the (Bosboom et al., 2009). Finally, previous rsMEG evidence showed a exaggerated amplitude of (1) beta rhythms detected in the cerebral widespread progressive slowing of brain rhythms in initially non- cortex, thalamus, and pedunculopontine nucleus (Devergnas et al., demented PD patients, with decreasing cognitive performance 2014; Geng et al., 2016) and (2) the abnormal coherence of these associated with increased delta and theta power, as well as de- beta rhythms between cerebral cortex and subthalamic nucleus creases in alpha and gamma power (Olde Dubbelink et al., 2013). In (Sharott et al., 2005) or pedunculopontine nucleus (Geng et al., that study, motor impairment was associated with a theta power 2016). increase only (Olde Dubbelink et al., 2013). Keeping in mind the present and previous findings, it can be speculated that the multiple oscillatory activities (e.g., from delta to 4.1. The neurophysiological model high-frequency gamma) recordable in the cerebral cortex, basal ganglia, and pedunculopontine loops may regulate thalamocortical At the present stage of the research, we cannot provide a re-entrant signals to the cerebral cortex and modulate the effi- conclusive explanation about the neurophysiologic underpinning of ciency of neural signal transfer underpinning different aspects of the reported findings. Rather, we can speculate based on known postural control and motor performance (e.g., postural adaptations fundamentals concerning the basal ganglia circuits and mecha- and gait, oculomotor control, sensorimotor integration, and nisms generating alpha rhythms. According to those fundamentals, voluntary movements). In this physiological framework, the PD- a daily dose of levodopa in a chronic regimen may induce an in- related dopaminergic depletion in the substantia nigra might be crease in cortical excitation in PDCD during a quiet wakefulness, as associated with a pathological enhancement in slow frequency (i.e., revealed by a reduction in widespread cortical alpha source activity. delta-theta) and beta rhythms in the functional connectivity be- This effect may depend on a complex modulation between thala- tween the cerebral cortex and subthalamus. This pathological mocortical and basal ganglia circuits that generate multiple brain enhancement in the cortical-basal ganglia-pedunculopontine sys- rhythms from delta to gamma frequencies; in this regard, previous tem may be partially recovered by daily doses of levodopa. In this studies have shown an interrelatedness of posterior alpha and regard, the present results suggest that the partial normalization of frontal beta rhythms between cerebral cortex and subthalamic low-frequency rhythms in the PD cerebral cortex can be non- nucleus in PD patients (Hirschmann et al., 2011, 2013; Litvan et al., invasively observed at delta band in quiet wakefulness. Moreover, a 2011) and relatedly frontal motor areas were shown to trigger the brain dopaminergic modulation might also induce an increase of suppression of interfering beta rhythms in the subthalamic nucleus cerebral arousal in the quiet wakefulness as revealed by the (Gaynor et al., 2008). reduction of alpha rhythms. This effect may facilitate higher In previous investigations, there were contrasting effects of sensorimotor information processes and transfer of motor com- levodopa administration on cortical rsEEG rhythms in PD patients mands. Future studies should clarify if these effects of the brain (Priori et al., 2004; Stoffers et al., 2007). In those patients, a daily dopaminergic modulation are mainly mediated by direct, indirect, dose of levodopa inhibited alpha rhythms in the cerebral cortex or hyperdirect pathways of basal ganglia (Albin et al., 2012; (including hemispherical asymmetry in occipital regions) in rela- Alexander et al., 1986; Wichmann et al., 2011). tion to clinical responses (Mostile et al., 2015). Furthermore, the dopaminergic therapy strongly promoted the interrelatedness of 5. Methodological remarks alpha rhythms between the cerebral cortex and pedunculopontine nucleus (Androulidakis et al., 2008). Moreover, the levodopa dose There are a number of methodological limitations to our study. also decreased exaggerated beta rhythms in the cerebral cortex First, the relatively small number of the patients in the PD, (Mostile et al., 2015) and subthalamic nucleus (Priori et al., 2004; PDMCI, and PDD groups (N ¼ 35-50) did not allow to adequately Wang et al., 2016). Among these effects, those on beta rhythms covary or contrast for a number of potential factors, which are were related to an improvement of motor symptoms and can known to affect the EEG such pharmacological intervention (e.g., represent an important candidate biomarker of PD (Wang et al., cholinergic, dopaminergic, serotoninergic), the severity of demen- 2016). In those previous investigations, the levodopa dose also tia and motor symptoms, and/or the disease duration. partially recovered some rsEEG rhythms such as suppressed theta In this retrospective study, data were collected without a single and exaggerated high-frequency gamma (110e170 Hz) recorded in experimental protocol in the clinical units. For this reason, some the thalamus (Kane et al., 2009) and subthalamic nucleus in rela- biomarkers, clinical indexes, and neuropsychological scores were tion to normative data (Priori et al., 2004; Wang et al., 2016). not available in all subjects, for example, APOE genotyping, DaTs- Compared with the levodopa dose, deep brain high-frequency can, and Alzheimer's Disease Assessment Scale-Cognitive Subscale stimulation (i.e., 130 Hz/2V) in the subthalamic nucleus decreased as a measurement of the global cognitive status. Indeed, the only both alpha and beta rhythms and increased delta-theta rhythms common measurement was the MMSE score (typically used for the recorded from that nucleus (Blumenfeld et al., 2015; Giannicola assessment of the global cognitive functions in elderly subjects, et al., 2012). Noteworthy, those exaggerated subthalamic beta with special attention to the area of memory). However, it may be rhythms were found to be triggered by primary and supplementary not equally sensitive to global cognitive deficits in all neurode- motor areas (Gaynor et al., 2008). generative dementing disorders. Previous findings in PD patients are globally corroborated by The subjects of this study did not receive identical instructions previous animal models of the disease. It was shown that the in all clinical units, and the experimenters did not receive the same hyperdirect pathway of basal ganglia may propagate exaggerated qualification training to set the environmental conditions for the cortical beta rhythms to the subthalamic nucleus under dopamine rsEEG recording. However, these aspects might not have a major depletion as “antimovement” consequences (Magill et al., 2001). impact on the final results as the experimental procedures of this Furthermore, the severity of experimental parkinsonism correlates study are very standard in the practice of the expert clinical units of with increases in theta and alpha power recorded in frontal motor the E-DLB and PDWAIVE Consortia. areas and internal global pallidus, in association with decreased In the clinical units, rsEEG activity was recorded using different cortical beta power (Devergnas et al., 2014). In those animal studies, hardware systems and various recording parameters (i.e., anti- a dose of levodopa reversed the exaggerated amplitude in alpha aliasing passband, frequency sampling, and reference electrode). To C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20 19 reduce potential sources of variability, data analysis was central- Gschwandtner, U., Ransmayr, G., Caravias, G., Garn, H., Sorpresi, F., Pievani, M., ’ ized: a common antialiasing bandpass filtering and downsampling D Antonio, F., De Lena, C., Güntekin, B., Hanoglu, L., Bas¸ ar, E., Yener, G., Emek- Savas¸ , D.D., Triggiani, A.I., Franciotti, R., Frisoni, G.B., Bonanni, L., De Pandis, M.F., to 128 Hz, a rereferencing of all rsEEG data to the common average 2017a. Abnormalities of cortical neural synchronization mechanisms in subjects reference, and a normalization of the eLORETA rsEEG sources to with mild cognitive impairment due to Alzheimer’s and Parkinson’s diseases: e remove the effects of the local amplifier gain and electrode an EEG study. J. Alzheimers Dis. 59, 339 358. Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Cordone, S., Lopez, S., Soricelli, A., resistance. Ferri, R., Pascarelli, M.T., Nobili, F., Arnaldi, D., Aarsland, D., Orzi, F., Buttinelli, C., Finally, the lack of serial rsEEG recordings within a day for a large Giubilei, F., Onofrj, M., Stocchi, F., Stirpe, P., Fuhr, P., Gschwandtner, U., cohort of PD patients and follow-ups did not allow the evaluation of Ransmayr, G., Caravias, G., Garn, H., Sorpresi, F., Pievani, M., Frisoni, G.B., ’ fl D Antonio, F., De Lena, C., Güntekin, B., Hanoglu, L., Bas¸ ar, E., Yener, G., Emek- the rsEEG uctuation as motor, cognitive, and behavioral Savas¸ , D.D., Triggiani, A.I., Franciotti, R., De Pandis, M.F., Bonanni, L., 2017b. symptoms. Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer’s and Lewy body diseases: an EEG study. Neurobiol. Aging 55, 143e158. 6. Conclusions Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Lopez, S., Soricelli, A., Ferri, R., Pascarelli, M.T., Catania, V., Nobili, F., Arnaldi, D., Famà, F., Orzi, F., Buttinelli, C., In this observational and retrospective study, we hypothesized Giubilei, F., Bonanni, L., Franciotti, R., Onofrj, M., Stirpe, P., Fuhr, P., Gschwandtner, U., Ransmayr, G., Garn, H., Fraioli, L., Pievani, M., D’Antonio, F., De that dopamine neuromodulation may affect cortical excitability in Lena, C., Güntekin, B., Hanoglu, L., Bas¸ ar, E., Yener, G., Emek-Savas¸ , D.D., PDCD set in quiet wakefulness, as revealed by rsEEG rhythms at Triggiani, A.I., Taylor, J.P., De Pandis, M.F., Vacca, L., Frisoni, G.B., Stocchi, F., alpha frequencies. Our results showed that widespread frontal and 2018a. Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild temporal alpha source activities exhibited an increase in magnitude cognitive impairment due to Alzheimer’s and Parkinson’s diseases. Clin. Neu- in the PDD compared with the PDMCI and PD groups, as a possible rophysiol. 129, 766e782. function of dopamine depletion severity, which is typically greater Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Lopez, S., Soricelli, A., Ferri, R., Nobili, F., Arnaldi, D., Famà, F., Aarsland, D., Orzi, F., Buttinelli, C., Giubilei, F., Onofrj, M., in the former than the latter groups. In this line, a daily dose of Stocchi, F., Stirpe, P., Fuhr, P., Gschwandtner, U., Ransmayr, G., Garn, H., Fraioli, L., levodopa (i.e., ON condition) induced a widespread reduction in Pievani, M., Frisoni, G.B., D’Antonio, F., De Lena, C., Güntekin, B., Hanoglu, L., cortical delta and alpha source activations in a subgroup of 13 PD Bas¸ ar, E., Yener, G., Emek-Savas¸ , D.D., Triggiani, A.I., Franciotti, R., Taylor, J.P., Vacca, L., De Pandis, M.F., Bonanni, L., 2018b. Abnormalities of resting-state patients under standard chronic dopaminergic regimen. Interest- functional cortical connectivity in patients with dementia due to Alzheimer’s ingly, this effect was related to relevant clinical and neuropsycho- and Lewy body diseases: an EEG study. Neurobiol. Aging 65, 18e40. logical scores. On the whole, these findings suggest that in PD Barker, R.A., Williams-Gray, C.H., 2016. Review. The spectrum of clinical features seen with alpha synuclein pathology. Neuropathol. Appl. Neurobiol. 42, 6e19. patients set in quiet wakefulness, alpha cortical source activations Blumenfeld, Z., Velisar, A., Miller Koop, M., Hill, B.C., Shreve, L.A., Quinn, E.J., may reflect an excitatory effect of dopamine neuromodulation. Kilbane, C., Yu, H., Henderson, J.M., Brontë-Stewart, H., 2015. Sixty hertz neu- Long-term impact of daily dopamine supplementation on cortical rostimulation amplifies subthalamic neural synchrony in Parkinson’s disease. excitability in PD patients should be tested in future studies. PLoS One 10, e0121067. Bonanni, L., Thomas, A., Tiraboschi, P., Perfetti, B., Varanese, S., Onofrj, M., 2008. EEG comparisons in early Alzheimer’s disease, dementia with Lewy bodies and ’ Acknowledgements Parkinson s disease with dementia patients with a 2-year follow-up. Brain 131 (Pt 3), 690e705. Bosboom, J.L., Stoffers, D., Stam, C.J., van Dijk, B.W., Verbunt, J., Berendse, H.W., The present study was developed based on the data of the Wolters, ECh, 2006. Resting state oscillatory brain dynamics in Parkinson’s e informal European Consortium PDWAVES and European Con- disease: an MEG study. Clin. Neurophysiol. 117, 2521 2531. Bosboom, J.L., Stoffers, D., Wolters, ECh, Stam, C.J., Berendse, H.W., 2009. MEG sortium of Dementia with Lewy Body. The members and institu- resting state functional connectivity in Parkinson’s disease related dementia. tional affiliations of the Consortia are reported in the cover page of J. Neural. Transm. 116, 193e202. this article. The research activities of the Unit of University of Rome Breslau, J., Starr, A., Sicotte, N., Higa, J., Buchsbaum, M.S., 1989. Topographic EEG “ ” changes with normal aging and SDAT. Electroencephalogr. Clin. Neurophysiol. La Sapienza were partially supported by the H2020 Marie S. Curie 72, 281e289. ITN-ETN project with the short title “BBDiag” (http://bbdiag-itn- Briel, R.C., McKeith, I.G., Barker, W.A., Hewitt, Y., Perry, R.H., Ince, P.G., Fairbairn, A.F., etn.eu). The authors thank Mrs. Jessica Janson and Mrs. Marina 1999. EEG findings in dementia with Lewy bodies and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 66, 401e403. Selivanova for their support to those activities in the framework of Buter, T.C., van den, H.A., Matthews, F.E., Larsen, J.P., Brayne, C., Aarsland, D., 2008. the BBDiag project. Dementia and survival in Parkinson disease: a 12-year population study. Neurology 70, 1017e1022. Caviness, J.N., Hentz, J.G., Evidente, V.G., Driver-Dunckley, E., Samanta, J., Mahant, P., References Connor, D.J., Sabbagh, M.N., Shill, H.A., Adler, C.H., 2007. Both early and late cognitive dysfunction affects the electroencephalogram in Parkinson’s disease. Aarsland, D., Litvan, I., Salmon, D., Galasko, D., Wentzel-Larsen, T., Larsen, J.P., 2003. Parkinsonism Relat. Disord. 13, 348e354. Performance on the dementia rating scale in Parkinson’s disease with dementia Caviness, J.N., Lue, L.F., Hentz, J.G., Schmitz, C.T., Adler, C.H.1, Shill, H.A., and dementia with Lewy bodies: comparison with progressive supranuclear Sabbagh, M.N., Beach, T.G., Walker, D.G., 2016. Cortical phosphorylated a-Syn- palsy and Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 74, 1215e1220. uclein levels correlate with brain wave spectra in Parkinson’s disease. Mov. Adamiak, U., Kaldonska, M., Klodowska-Duda, G., Wyska, E., Safranow, K., Disord. 31, 1012e1019. Bialecka, M., Gawronska-Szklarz, B., 2010. Pharmacokinetic-pharmacodynamic Costa, E.R., Weinhold, P., Tayrose, G.A., Hooker, J.A., Dahners, L.E., 2006. The effect of modeling of levodopa in patients with advanced Parkinson disease. Clin. Neu- levodopa or levodopa-carbidopa (sinemet) on fracture healing. J. Orthop. ropharmacol. 33, 135e141. Trauma 20, 470e475. Albin, R.L., Young, A.B., Penney, J.B., 2012. The functional anatomy of basal ganglia Cummings, J.L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D.A., disorders. Trends Neurosci. 12, 366e375. Gornbein, J., 1994. The neuropsychiatric inventory: comprehensive assessment Alexander, G.E., DeLong, M.R., Strick, P.L., 1986. Parallel organization of functionally of psychopathology in dementia. Neurology 44, 2308e2314. segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, Dauwan, M., van der Zande, J.J., van Dellen, E., Sommer, I.E., Scheltens, P., 357e381. Lemstra, A.W., Stam, C.J., 2016. Random forest to differentiate dementia with Androulidakis, A.G., Mazzone, P., Litvak, V., Penny, W., Dileone, M., Gaynor, L.M., Lewy bodies from Alzheimer’s disease. Alzheimers Dement. (Amst). 4, 99e106. Tisch, S., Di Lazzaro, V., Brown, P., 2008. Oscillatory activity in the pedun- DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L., 1988. Comparing the areas under culopontine area of patients with Parkinson’s disease. Exp. Neurol. 211, two or more correlated receiver operating characteristic curves: a nonpara- 59e66. metric approach. Biometrics 44, 837e845. Babiloni, C., Lizio, R., Marzano, N., Capotosto, P., Soricelli, A., Triggiani, A.I., Devergnas, A., Pittard, D., Bliwise, D., Wichmann, T., 2014. Relationship between Cordone, S., Gesualdo, L., Del Percio, C., 2015. Brain neural synchronization and oscillatory activity in the cortico-basal ganglia network and parkinsonism in functional coupling in Alzheimer’s disease as revealed by resting state EEG MPTP-treated monkeys. Neurobiol. Dis. 68, 156e166. rhythms. Int. J. Psychophysiol. 103, 88e102. Dubois, B., Slachevsky, A., Litvan, I., Pillon, B., 2000. The FAB: a frontal assessment Babiloni, C., Del Percio, C., Lizio, R., Noce, G., Cordone, S., Lopez, S., Soricelli, A., battery at bedside. Neurology 55, 1621e1626. Ferri, R., Pascarelli, M.T., Nobili, F., Arnaldi, D., Famà, F., Aarsland, D., Orzi, F., Engedal, K., Snaedal, J., Hoegh, P., Jelic, V., Bo Andersen, B., Naik, M., Wahlund, L.O., Buttinelli, C., Giubilei, F., Onofrj, M., Stocchi, F., Stirpe, P., Fuhr, P., Oeksengaard, A.R., 2015. Quantitative EEG applying the statistical recognition 20 C. Babiloni et al. / Neurobiology of Aging 73 (2019) 9e20

pattern method: a useful tool in dementia diagnostic workup. Dement. Geriatr. Violante, M., Serrano-Dueñas, M., Ballesteros, D., Rojo-Abuin, J.M., Cogn. Disord. 40, 1e12. Chaudhuri, K.R., Merello, M., 2015. Parkinson’s disease severity levels and Fahn, S., Elton, R., UPDRS Development Committee, 1987. Unified Parkinson’s dis- MDS-Unified Parkinson’s Disease Rating Scale. Parkinsonism Relat. Disord. 21, ease rating scale. In: Fahn, S., Marsden, C.D., Calne, D.B., Goldstein, M. (Eds.), 50e54. Recent Developments in Parkinson’s Disease, 2. Macmillan Health Care Infor- McKeith, I.G., Galasko, D., Kosaka, K., Perry, E.K., Dickson, D.W., Hansen, L.A., mation, Florham Park, NJ, pp. 153e163. Salmon, D.P., Lowe, J., Mirra, S.S., Byrne, E.J., Lennox, G., Quinn, N.P., Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. “Mini-mental” state. A practical Edwardson, J.A., Ince, P.G., Bergeron, C., Burns, A., Miller, B.L., Lovestone, S., method for grading the cognitive state of patients for the clinician. J. Psychiatr. Collerton, D., Jansen, E.N., Ballard, C., de Vos, R.A., Wilcock, G.K., Jellinger, K.A., Res. 12, 189e198. Perry, R.H., 1996. Consensus guidelines for the clinical and pathologic diagnosis Fünfgeld, E.W., 1995. Computerised brain electrical activity findings of Parkinson of dementia with Lewy bodies (DLB): report of the consortium on DLB inter- patients suffering from hyperkinetic side effects (hypersensitive dopamine syn- national workshop. Neurology 47, 1113e1124. drome) and a review of possible sources. J. Neural Transm. Suppl. 46, 351e365. McKeith, I.G., Boeve, B.F., Dickson, D.W., Halliday, G., Taylor, J.P., Weintraub, D., Garn, H., Coronel, C., Waser, M., Caravias, G., Ransmayr, G., 2017. Differential diag- Aarsland, D., Galvin, J., Attems, J., Ballard, C.G., Bayston, A., Beach, T.G., Blanc, F., nosis between patients with probable Alzheimer’s disease, Parkinson’s disease Bohnen, N., Bonanni, L., Bras, J., Brundin, P., Burn, D., Chen-Plotkin, A., dementia, or dementia with Lewy bodies and frontotemporal dementia, Duda, J.E., El-Agnaf, O., Feldman, H., Ferman, T.J., Ffytche, D., Fujishiro, H., behavioral variant, using quantitative electroencephalographic features. Galasko, D., Goldman, J.G., Gomperts, S.N., Graff-Radford, N.R., Honig, L.S., J. Neural Transm. (Vienna) 124, 569e581. Iranzo, A., Kantarci, K., Kaufer, D., Kukull, W., Lee, V.M.Y., Leverenz, J.B., Gaynor, L.M., Kühn, A.A., Dileone, M., Litvak, V., Eusebio, A., Pogosyan, A., Lewis, S., Lippa, C., Lunde, A., Masellis, M., Masliah, E., McLean, P., Androulidakis, A.G., Tisch, S., Limousin, P., Insola, A., Mazzone, P., Di Lazzaro, V., Mollenhauer, B., Montine, T.J., Moreno, E., Mori, E., Murray, M., O’Brien, J.T., Brown, P., 2008. Suppression of beta oscillations in the subthalamic nucleus Orimo, S., Postuma, R.B., Ramaswamy, S., Ross, O.A., Salmon, D.P., Singleton, A., following cortical stimulation in humans. Eur. J. Neurosci. 28, 1686e1695. Taylor, A., Thomas, A., Tiraboschi, P., Toledo, J.B., Trojanowski, J.Q., Tsuang, D., Gelb, D.J., Oliver, E., Gilman, S., 1999. Diagnostic criteria for Parkinson disease. Arch. Walker, Z., Yamada, M., Kosaka, K., 2017. Diagnosis and management of de- Neurol. 56, 33e39. mentia with Lewy bodies: Fourth consensus report of the DLB Consortium. Geng, X., Wang, X., Xie, J., Zhang, X., Wang, X., Hou, Y., Lei, C., Li, M., Han, H., Yao, X., Neurology 89, 88e100. Zhang, Q., Wang, M., 2016. Effect of l-DOPA on local field potential relationship Melgari, J.M., Curcio, G., Mastrolilli, F., Salomone, G., Trotta, L., Tombini, M., di between the pedunculopontine nucleus and primary motor cortex in a rat Biase, L., Scrascia, F., Fini, R., Fabrizio, E., Rossini, P.M., Vernieri, F., 2014. Alpha model of Parkinson’s disease. Behav. Brain Res. 315, 1e9. and beta EEG power reflects L-dopa acute administration in parkinsonian pa- Geser, F., Wenning, G.K., Poewe, W., McKeith, I., 2005. How to diagnose dementia tients. Front. Aging Neurosci. 6, 302. with Lewy bodies: state of the art. Mov. Disord. 20 (Suppl 12), S11eS20. Moretti, D.V., Babiloni, F., Carducci, F., Cincotti, F., Remondini, E., Rossini, P.M., Giannicola, G., Rosa, M., Servello, D., Menghetti, C., Carrabba, G., Pacchetti, C., Salinari, S., Babiloni, C., 2003. Computerized processing of EEG-EOG-EMG arti- Zangaglia, R., Cogiamanian, F., Scelzo, E., Marceglia, S., Rossi, L., Priori, A., 2012. facts for multicentric studies in EEG oscillations and event-related potentials. Subthalamic local field potentials after seven-year deep brain stimulation in Int. J. Psychophysiol. 47, 199e216. Parkinson’s disease. Exp. Neurol. 237, 312e317. Moretti, D.V., Babiloni, C., Binetti, G., Cassetta, E., Dal Forno, G., Ferreric, F., Ferri, R., Hely, M.A., Reid, W.G., Adena, M.A., Halliday, G.M., Morris, J.G., 2008. The Sydney Lanuzza, B., Miniussi, C., Nobili, F., Rodriguez, G., Salinari, S., Rossini, P.M., 2004. multicenter study of Parkinson’s disease: the inevitability of dementia at 20 Individual analysis of EEG frequency and band power in mild Alzheimer’s dis- years. Mov. Disord. 23, 837e844. ease. Clin. Neurophysiol. 115, 299e308. Hirschmann, J., Özkurt, T.E., Butz, M., Homburger, M., Elben, S., Hartmann, C.J., Mostile, G., Nicoletti, A., Dibilio, V., Luca, A., Pappalardo, I., Giuliano, L., Cicero, C.E., Vesper, J., Wojtecki, L., Schnitzler, A., 2011. Distinct oscillatory STN-cortical loops Sciacca, G., Raciti, L., Contrafatto, D., Bruno, E., Sofia, V., Zappia, M., 2015. Elec- revealed by simultaneous MEG and local field potential recordings in patients troencephalographic lateralization, clinical correlates and pharmacological with Parkinson’s disease. Neuroimage 55, 1159e1168. response in untreated Parkinson’s disease. Parkinsonism Relat. Disord. 21, Hirschmann, J., Hartmann, C.J., Butz, M., Hoogenboom, N., Ozkurt, T.E., Elben, S., 948e953. Vesper, J., Wojtecki, L., Schnitzler, A., 2013. A direct relationship between Muslimovic, D., Post, B., Speelman, J.D., Schmand, B., 2005. Cognitive profile of oscillatory subthalamic nucleus-cortex coupling and rest tremor in Parkinson’s patients with newly diagnosed Parkinson disease. Neurology 65, 1239e1245. disease. Brain 136 (Pt 12), 3659e3670. Nasreddine, Z.S., Phillips, N.A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Hoehn, M., Yahr, M., 1967. Parkinsonism: onset, progression and mortality. Cummings, J.L., Chertkow, H., 2005. The Montreal Cognitive Assessment, MoCA: Neurology 17, 427e442. a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53, Hughes, T.A., Ross, H.F., Musa, S., Bhattacherjee, S., Nathan, R.N., Mindham, R.H., 695e699. Spokes, E.G., 2000. A 10-year study of the incidence of and factors predicting Olde Dubbelink, K.T., Stoffers, D., Deijen, J.B., Twisk, J.W., Stam, C.J., Berendse, H.W., dementia in Parkinson’s disease. Neurology 54, 1596e1602. 2013. Cognitive decline in Parkinson’s disease is associated with slowing of Irwin, D.J., Grossman, M., Weintraub, D., Hurtig, H.I., Duda, J.E., Xie, S.X., Lee, E.B., resting-state brain activity: a longitudinal study. Neurobiol. Aging 34, Van Deerlin, V.M., Lopez, O.L., Kofler, J.K., Nelson, P.T., Jicha, G.A., Woltjer, R., 408e418. Quinn, J.F., Kaye, J., Leverenz, J.B., Tsuang, D., Longfellow, K., Yearout, D., Pascual-Marqui, R.D., 2007. Discrete, 3D distributed, linear imaging methods of electric Kukull, W., Keene, C.D., Montine, T.J., Zabetian, C.P., Trojanowski, J.Q., 2017. neuronal activity. Part 1: exact, zero error localization arXiv:0710.3341 [math-ph]. Neuropathological and genetic correlates of survival and dementia onset in Available at. http://arxiv.org/pdf/0710.3341. (Accessed 19 November 2015). synucleinopathies: a retrospective analysis. Lancet Neurol. 16, 55e65. Priori, A., Foffani, G., Pesenti, A., Tamma, F., Bianchi, A.M., Pellegrini, M., Locatelli, M., Johns, M.W., 1991. A new method for measuring daytime sleepiness: the Epworth Moxon, K.A., Villani, R.M., 2004. Rhythm-specific pharmacological modulation sleepiness scale. Sleep 14, 540e545. of subthalamic activity in Parkinson’s disease. Exp. Neurol. 189, 369e379. Kamei, S., Morita, A., Serizawa, K., Mizutani, T., Hirayanagi, K., 2010. Quantitative Pugnetti, L., Baglio, F., Farina, E., Alberoni, M., Calabrese, E., Gambini, A., Di Bella, E., EEG analysis of executive dysfunction in Parkinson disease. J. Clin. Neurophysiol. Garegnani, M., Deleonardis, L., Nemni, R., 2010. EEG evidence of posterior 27, 193e197. cortical disconnection in PD and related dementias. Int. J. Neurosci. 120, 88e98. Kane, A., Hutchison, W.D., Hodaie, M., Lozano, A.M., Dostrovsky, J.O., 2009. Dopa- Serizawa, K., Kamei, S., Morita, A., Hara, M., Mizutani, T., Yoshihashi, H., mine-dependent high-frequency oscillatory activity in thalamus and sub- Yamaguchi, M., Takeshita, J., Hirayanagi, K., 2008. Comparison of quantitative thalamic nucleus of patients with Parkinson’s disease. Neuroreport 20, EEGs between Parkinson disease and age-adjusted normal controls. J. Clin. 1549e1553. Neurophysiol. 25, 361e366. Klimesch, W., 1996. Memory processes, brain oscillations and EEG synchronization. Sharott, A., Magill, P.J.,Harnack, D., Kupsch, A., Meissner, W., Brown, P., 2005. Dopamine Int. J. Psychophysiol. 24, 61e100. depletion increases the power and coherence of beta-oscillations in the cerebral Klimesch, W., Doppelmayr, M., Russegger, H., Pachinger, T., Schwaiger, J., 1998. cortex and subthalamic nucleus of the awake rat. Eur. J. Neurosci. 21, 1413e1422. Induced alpha band power changes in the human EEG and attention. Neurosci. Simon, N., Viallet, F., Boulamery, A., Eusebio, A., Gayraud, D., Azulay, J.P., 2016. Lett. 244, 73e76. A combined pharmacokinetic/pharmacodynamic model of levodopa motor Klimesch, W., 1999. EEG alpha and theta oscillations reflect cognitive and memory response and dyskinesia in Parkinson’s disease patients. Eur. J. Clin. Pharmacol. performance: a review and analysis. Brain Res. Rev. 29, 169e195. 72, 423e430. Levy, G., Tang, M.X., Cote, L.J., Louis, E.D., Alfaro, B., Mejia, H., Stern, Y., Marder, K., Stoffers, D., Bosboom, J.L., Deijen, J.B., Wolters, E.C., Berendse, H.W., Stam, C.J., 2007. 2000. Motor impairment in PD: relationship to incident dementia and age. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s Neurology 55, 539e544. disease without dementia. Brain 130 (Pt 7), 1847e1860. Litvan, I., Aarsland, D., Adler, C.H., Goldman, J.G., Kulisevsky, J., Mollenhauer, B., Wang, D.D., de Hemptinne, C., Miocinovic, S., Qasim, S.E., Miller, A.M., Ostrem, J.L., Rodriguez-Oroz, M.C., Tröster, A.I., Weintraub, D., 2011. MDS Task Force on mild Galifianakis, N.B., San Luciano, M., Starr, P.A., 2016. Subthalamic local field po- cognitive impairment in Parkinson’s disease: critical review of PD-MCI. Mov. tentials in Parkinson’s disease and isolated dystonia: an evaluation of potential Disord. 26, 1814e1824. biomarkers. Neurobiol. Dis. 89, 213e222. Magill, P.J., Bolam, J.P., Bevan, M.D., 2001. Dopamine regulates the impact of the Wichmann, T., DeLong, M.R., Guridi, J., Obeso, J.A., 2011. Milestones in research on cerebral cortex on the subthalamic nucleus-globus pallidus network. Neuro- the pathophysiology of Parkinson’s disease. Mov Disord. 26, 1032e1041. science 106, 313e330. Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., Leirer, V.O., 1983. Martínez-Martín, P., Rodríguez-Blázquez, C., Alvarez, Mario, Arakaki, T., Arillo, V.C., Development and validation of a geriatric depression screening scale: a pre- Chaná, P., Fernández, W., Garretto, N., Martínez-Castrillo, J.C., Rodríguez- liminary report. J. Psychiatr. Res. 17, 37e49.