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Journal of Alzheimer’s Disease 25 (2011) 1–16DOI 10.3233/JAD-2011-101710IOS Press

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Resting State CorticalElectroencephalographic Rhythmsand White Matter Vascular Lesionsin Subjects with Alzheimer’s Disease:An Italian Multicenter Study

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Claudio Babilonia,b,∗, Roberta Lizioc, Filippo Carduccid, Fabrizio Vecchioe, Alberto Redolfif ,Silvia Marinog, Gioacchino Tedeschih,i, Patrizia Montellah, Antonio Guizzaroh, Fabrizio Esposito, j,Alessandro Bozzaok, Franco Giubileik, Francesco Orzik, Carlo C. Quattrocchil, Andrea Soricellim,Elena Salvatorem, Annalisa Baglierig, Placido Bramantig, Marina Boccardif , Raffaele Ferrin,Filomena Cosentinon, Michelangelo Ferraran, Ciro Mundio, Gianpaolo Grillio, Silvia Pugliesed,Gianluca Gerardid, Laura Parisie, Fabrizio Vernieril, Antonio I.Triggiania, Jan T. Pedersenp,Hans-Goran Hardemarkq, Paolo M. Rossinib,r and Giovanni B. Frisonif

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aDepartment of Biomedical Sciences, University of Foggia, Foggia, Italy13

bDepartment of Imaging, SAN RAFFAELE Cassino, Italy14

cIRCCS San Raffaele Pisana, Rome, Italy15

dLaboratory of Computational Neuroanatomy, Department of Physiology and Pharmacology,University of Rome “Sapienza”, Rome, Italy

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eA.Fa.R., Dip. Neurosci. Osp. FBF; Isola Tiberina, Rome, Italy18

f IRCCS “S. Giovanni di Dio-F.B.F.”, Brescia, Italy19

gIRCCS Centro Neurolesi “Bonino-Pulejo” – Messina, Italy20

hDepartment of Neurological Sciences, Second University of Naples, Naples, Italy21

iNeurological Institute for Diagnosis and Care “Hermitage Capodimonte”, Naples, Italy22

jDepartment of Neuroscience, University of Naples “Federico II”, Naples, Italy23

kAzienda Ospedaliera Sant’Andrea Universita di Roma “Sapienza”, Roma, Italy24

lIRCCS “Ospedale Pediatrico Bambino Gesu”, Roma, Italy25

mFondazione SDN per la Ricerca e l’Alta Formazione in Diagnostica Nucleare, IRCCS Naples, Italy26

nIRCCS Oasi, Troina (Enna), Italy27

oDepartment of Neuroscience, Ospedali Riuniti di Foggia, Foggia, Italy28

pSection head, Bioinformatics, H. Lundbeck A/S, Valby, Denmark29

qAstrazeneca, Stockholm, Swerige30

rNeurol. University “Campus Biomedico” Rome, Italy31

Accepted 9 April 2011

∗Correspondence to: Prof. Claudio Babiloni, PhD, Departmentof Biomedical Sciences, University of Foggia, Viale Pinto 7, Fog-gia I-71100, Italy. Tel. and Fax: +39 0881 713276 (1716); E-mail:c.babiloni@unifg.it.

ISSN 1387-2877/11/$27.50 © 2011 – IOS Press and the authors. All rights reserved

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2 C. Babiloni et al. / EEG and Vascular Lesion in AD

Abstract. Resting state electroencephalographic (EEG) rhythms do not deteriorate with the increase of white matter vascularlesion in amnesic mild cognitive impairment (MCI) subjects [1], although white matter is impaired along Alzheimer’s disease(AD). Here we tested whether this is true even in AD subjects. Closed-eye resting state EEG data were recorded in 40 healthyelderly (Nold), 96 amnesic MCI, and 83 AD subjects. White matter vascular lesions were indexed by magnetic resonance imagingrecorded in the MCI and AD subjects (about 42% of cases following ADNI standards). The MCI subjects were divided into twosub-groups based on the median of the white matter lesion, namely MCI+ (people with highest vascular load; n = 48) and MCI−(people with lowest vascular load; n = 48). The same was true for the AD subjects (AD+, n = 42; AD−, n = 41). EEG rhythms ofinterest were delta (2–4 Hz), theta (4–8 Hz), alpha1 (8–10.5 Hz), alpha2 (10.5–13 Hz), beta1 (13–20 Hz), beta2 (20–30 Hz), andgamma (30–40 Hz). LORETA software estimated cortical EEG sources. When compared to Nold group, MCI and AD groupsshowed well known abnormalities of delta and alpha sources. Furthermore, amplitude of occipital, temporal, and limbic alpha 1sources were higher in MCI+ than MCI− group. As a novelty, amplitude of occipital delta sources was lower in AD+ than AD−group. Furthermore, central, parietal, occipital, temporal, and limbic alpha sources were higher in amplitude in AD+ than AD−group. Amplitude of these sources was correlated to global cognitive status (i.e., Mini Mental State Evaluation score). Theseresults suggest that in amnesic MCI and AD subjects, resting state posterior delta and alpha EEG rhythms do not deterioratewith the increase of white-matter vascular lesion. These rhythms might be more sensitive to AD neurodegenerative processesand cognitive status rather than to concomitant lesions to white matter.

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Keywords: Alzheimer’s disease, Alzheimer’s disease neuroimaging initiative, amnesic mild c cognitive impairment, electroen-cephalographic rhythms, low resolution brain electromagnetic tomography, magnetic resonance imaging, resting state, whitematter vascular lesion48

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INTRODUCTION32

Previous studies in Alzheimer’s disease (AD) and33

amnesic mild cognitive impairment (MCI) subjects34

have shown that resting state closed-eye EEG rhythms35

may be promising markers of disease when evaluated36

by quantitative methods. When compared to normal37

elderly (Nold) subjects, AD patients have been char-38

acterized by high power of delta (0–4 Hz) and theta39

(4–7 Hz) rhythms, and low power of posterior alpha40

(8–12 Hz) and/or beta (13–30 Hz) rhythms [2–8]. In41

line with the “transition” hypothesis, amnesic MCI42

subjects have displayed increased theta power [9–11]43

as well as decreased alpha power [4, 8, 10–15]. Further-44

more, power of resting state alpha rhythms in amnesic45

MCI subjects has been found to be intermediate with46

respect to that of Nold and AD subjects [6, 12, 14].47

A bulk of previous evidence indicates that power48

of resting state eyes closed EEG rhythms reflect neu-49

rodegenerative processes in amnesic MCI and AD50

subjects [4, 8, 9, 12, 14, 16]. First, in MCI and AD sub-51

jects, abnormalities of EEG rhythms were associated to52

typical signs of neurodegeneration such as hippocam-53

pal atrophy [17] and impairment of the cholinergic54

tracts from basal forebrain to cerebral cortex [17, 18].55

Second, these abnormalities were also associated to56

altered regional cerebral blood flow/metabolism and57

to impaired global cognitive function in MCI or AD58

subjects [1, 6, 14, 19–21]. Third, decrement of pos-

terior alpha power showed peculiar features in AD 59

subjects when compared to cerebrovascular demen- 60

tia subjects with similar cognitive impairment [7]. 61

Fourth, posterior alpha power was relatively preserved 62

in amnesic MCI subjects in whom cognitive decline 63

was mainly explained by white-matter vascular lesion, 64

thus suggesting that these rhythms are less affected 65

by diffuse white matter vascular lesions than parallel 66

neurodegenerative processes [1, 23]. This hypothe- 67

sis is in line with recent evidence showing that there 68

were fewer neurodegenerative lesions in AD patients 69

with vascular lesions than in those without vascular 70

lesions, suggesting that neurodegenerative and cere- 71

brovascular lesions act as additive/synergistic causes 72

of AD [24–26]. On the other hand, several field studies 73

have reported some interactions between AD and cere- 74

brovascular function. Clinical and cognitive status of 75

AD patients was in part explained by amyloid angiopa- 76

thy of small vessels [27]. Furthermore, AD patients 77

carrying ApoE4 allele as a genetic risk of AD presented 78

an increment of intima-media thickness values with 79

respect to non-carriers and cerebrovascular dementia 80

patients [28]. Finally, evolution of cognitive function 81

in AD was unfavorable as a function of impaired cere- 82

bral vasomotor reactivity [29]. Keeping in mind these 83

data and considerations, current evidence suggests that 84

cerebrovascular dysfunction precedes and accompa- 85

nies cognitive dysfunction and AD neurodegeneration, 86

although its impact on the abnormalities of resting state 87

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C. Babiloni et al. / EEG and Vascular Lesion in AD 3

Table 1Demographic and clinical data of healthy elderly (Nold), mild cognitive impairment (MCI), and mild Alzheimer’s disease (AD) subjects

Subjects (n) Gender (M/F) Age (years) MMSE IAF (Hz) IADL CDR

Nold 40 22/18 72.1 ± (1.0) 27.7 ± (0.2 SE) 9.3 ± (0.2) – –MCI 96 32/64 71.4 ± (1.0) 25.9 ± (0.3 SE) 9.5±(0.1) 2.3 (±0.1 SE) 0.3 (±0.06 SE)AD 83 25/58 69.8 ± (0.8) 19.6 ± (0.5 SE) 8.8 ± (0.2) 4.5 (±0.3 SE) 1.0 (±0.1 SE)

EEG rhythms in AD might be negligible. To address88

to this issue, the present study tested the hypothesis89

that in both amnesic MCI and AD subjects, posterior90

resting state EEG rhythms do not deteriorate with the91

increase of white-matter vascular lesion, according to92

the idea that these rhythms are less affected by such93

vascular than neurodegenerative processes.94

METHODS95

Subjects96

In this study, 96 amnesic MCI subjects and 8397

AD patients were recruited. Furthermore, 40 cogni-98

tively intact elderly (Nold) subjects were selected as99

a control group. The Nold subjects globally matched100

the personal variables of the MCI and AD subjects.101

Table 1 reports demographic and clinical data of the102

AD, amnesic MCI, and Nold groups.103

The study was approved by the local Institutional104

ethics committee, and follows prescriptions of the105

Good Clinical Practice (GCP); informed and overt con-106

sent of subjects or subjects’ legal representatives, in107

line with the Code of Ethics of the World Medical108

Association (Declaration of Helsinki) and the stan-109

dards established by the Author’s Institutional Review110

Board.111

Diagnostic criteria112

The present inclusion and exclusion criteria for113

amnesic MCI subjects were based on international114

standards [30–39]. Summarizing, the inclusion criteria115

were as follows: (i) objective memory impairment on116

ADNI neuropsychological evaluation probing cogni-117

tive performance in the domains of memory, language,118

executive function/attention, etc; (ii) normal activities119

of daily living as documented by the history and evi-120

dence of independent living; and (iii) clinical dementia121

rating score of 0.5.122

The exclusion criteria included: (i) mild dementia123

of the AD type, as diagnosed by standard proto-124

cols including NINCDS-ADRDA [40] and DSM-IV;125

(ii) evidence (including magnetic resonance imag-126

ing – MRI – procedures) of concomitant cerebral 127

impairment such as frontotemporal degeneration, cere- 128

brovascular disease with large vascular lacunar lesions 129

in gray or white matter, and reversible cognitive 130

impairment (including pseudo-depressive dementia); 131

(iii) marked fluctuations in cognitive performance 132

compatible with Lewy body dementia and/or features 133

of mixed cognitive impairment including cerebrovas- 134

cular disease (particular attention was devoted to this 135

point given the working hypothesis focused on cog- 136

nitive stability in MCI subjects); (iv) evidence of 137

concomitant extra-pyramidal symptoms; (v) clinical 138

and indirect evidence of depression as revealed by the 139

Geriatric Depression Scale (GDS; [41]) scores >14; 140

(vi) other psychiatric diseases, epilepsy, drug addic- 141

tion, alcohol dependence (as revealed by a psychiatric 142

interview) and use of psychoactive drugs including 143

acetylcholinesterase inhibitors or other drugs enhanc- 144

ing brain cognitive functions; and (vii) current or 145

previous uncontrolled or complicated systemic dis- 146

eases (including diabetes mellitus) or traumatic brain 147

injuries. 148

Probable AD was diagnosed according to NINCDS- 149

ADRDA [40] and DSM IV criteria. The recruited AD 150

patients underwent general medical, neurological, neu- 151

ropsychological, and psychiatric assessments. Patients 152

were rated with a number of standardized diagnostic 153

and severity instruments that included Mini Mental 154

State Evaluation (MMSE; [42]), Clinical Demen- 155

tia Rating Scale (CDR; [43]), GDS [41], Hachinski 156

Ischemic Score (HIS, [44]), and Instrumental Activi- 157

ties of Daily Living scale (IADL, [45]). Neuroimaging 158

diagnostic procedures (MRI) and complete laboratory 159

analyses were carried out to exclude other causes of 160

progressive or reversible dementias. Exclusion criteria 161

included any evidence of (i) frontotemporal dementia, 162

diagnosed according to current criteria [46], (ii) MRI 163

of cerebrovascular disease with large vascular lacunar 164

lesions in gray or white matter (iii) vascular demen- 165

tia, diagnosed according to NINDS-AIREN criteria 166

[47], (iv) extra-pyramidal syndromes, (iv) reversible 167

dementias (including pseudodementia of depression); 168

and (v) Lewy body dementia, according to the criteria 169

by [48]. 170

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4 C. Babiloni et al. / EEG and Vascular Lesion in AD

The present Nold subjects were recruited mostly171

from non-consanguineous patients’ relatives. All Nold172

subjects underwent physical and neurological exam-173

inations as well as cognitive screening (including174

MMSE and GDS). Subjects affected by chronic sys-175

temic illnesses, those receiving psychoactive drugs, or176

with a history of neurological or psychiatric disease177

were excluded. All Nold subjects had a GDS score178

lower than 14 (no depression).179

Magnetic Resonance Imaging (MRI)180

Three-D proton density (PD), T1- and T2-weighted181

volumetric MRIs were recorded by the clinical units182

of the present Italian multi-centric study (University183

of Foggia-Ospedali Riuniti di Foggia; San Raffaele184

Cassino; Isola Tiberina Fatebenefratelli Hospital,185

Rome; IRCCS Fatebenefratelli Brescia; IRCCS Centro186

Neurolesi, Messina; Azienda Ospedaliera Sant’Andrea187

University of Rome “Sapienza”; University of Naples188

“Federico II”; Second University of Naples; Uni-189

versity “Campus Biomedico” Rome; IRCCS and190

Fondazione SDN Naples; IRCCS Oasi, Troina). Some191

of these units (IRCCS Centro Neurolesi “Bonino-192

Pulejo”, Messina; Azienda Ospedaliera Sant’Andrea193

University of Rome “Sapienza”; University of Naples194

“Federico II”; Second University of Naples; University195

“Campus Biomedico” Rome; IRCCS and Fondazione196

SDN Naples; IRCCS Oasi, Troina) collected the197

MRIs following the ADNI protocol (http://www.adni-198

info.org/). In total, about 42% of the whole dataset was199

collected according to the ADNI project.200

Analysis of the 3-D PD, T1-, and T2-weighted vol-201

umetric MRIs was centralized at University of Rome202

“Sapienza”. The MRIs were visually inspected to ver-203

ify the absence of structural abnormalities or technical204

artifacts. Afterwards, they were given as an input to205

Expectation-Maximization Segmentation (EMS) soft-206

ware, which is an SPM99 tool (Wellcome Dept. Cogn.207

Neurol., London; http://www.fil.ion.ucl.ac.uk/spm)208

running under MATLAB 7.0 (MathWorks, Natick,209

MA). On the whole, the EMS tool performs (i) an auto-210

mated, atlas-based classification of brain tissue from211

3-D PD, T1- and T2-weighted volumetric MRIs, (ii)212

builds a stochastic individual model of “normal” tis-213

sue intensity at voxel level on the basis of all MRIs, (iii)214

detects voxels with “vascular lesion” by the computa-215

tion of the Mahalanobis distance. In detail, individual216

MRIs were corrected for field inhomogeneities and217

coregistered each other. The coregistered MRIs were218

normalized to the SPM99 T1 template, which allowed219

the classification of the voxels into three compartments220

including gray matter, white matter or cerebral-spinal 221

fluid. Afterwards, the EMS tool estimated the param- 222

eters of a stochastic model of tissue intensity for 223

“normal” brain MRIs in each individual normalized 224

dataset. Tissue intensities for the “normal” brain model 225

were represented with a 3-classes (i.e., gray matter, 226

white matter, and cerebral-spinal fluid) finite multi- 227

variate Gaussian mixture. All MRI sequences (i.e., 228

3-D PD, T1-, and T2-weighted) were used to create 229

a multidimensional feature space, in order to benefit 230

of the specific inherent information of each sequence. 231

These sequences were iteratively classified into a small 232

number of Gaussian distributions. During this iterative 233

process, the EMS tool rejected voxels that exceed a 234

predefined Mahalanobis distance to each of the Gaus- 235

sians, and updated the model parameters only based 236

on non-rejected voxels [49]. Vascular lesion of white 237

matter was defined as the amount of voxels classified 238

as affected by vascular lesion and rejected from the 239

stochastic model of “normal brain”, according to the 240

mentioned Mahalanobis distance. In this framework, 241

the use of Markov random fields (MRF) discouraged a 242

voxel to be classified as brain lesion in the absence of 243

neighboring white matter. 244

Of note, the EMS tool implements an automated 245

procedure that requires only the Mahalanobis distance 246

threshold parameter (k) to be computed on the basis 247

of a variable defined by the experiment; namely, the 248

parameter k determined the significance level at which 249

voxels are considered as model outliers. An appropri- 250

ate k value had to be chosen in advance by means of an 251

experimentally tuned procedure, because of the choice 252

of k significantly affects the quality of the brain lesion 253

segmentation [49, 50]. The optimal value of the param- 254

eter k was identified on 36 MRI individual datasets. 255

The MRI segmentations were obtained with the auto- 256

matic tool varying the values of k from 3.0 to 5.0 with 257

steps of 0.5. The results were correlated to those of the 258

MRI segmentation performed by our expert neurora- 259

diologists. The highest correlation was obtained with 260

parameter k equal to 4.0 (Pearson r = 0.7; p = 0.001), 261

which is exactly the threshold value suggested by the 262

researcher who developed the EMS tool for the detec- 263

tion of vascular lesions in multiple sclerosis patients 264

(http://www.medicalimagecomputing.com). 265

Based on the above procedure for the estimation 266

of white-matter vascular lesions, the MCI subjects 267

were divided into two sub-groups. The median of the 268

white matter lesion was used as the criterion of the 269

definition of MCI people with highest vascular load 270

or MCI + (≥4960 voxels; mean of 7479 voxels ± 483 271

standard error, SE; n = 48) and of MCI people with 272

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Table 2Demographic and clinical data of the following sub-groups: AD with low degree of white-matter lesion (AD−, normalized white-matter vascularlesions <3430); AD with high degree of white-matter lesion (AD+, normalized white-matter vascular lesions ≥3430); MCI with low degreeof white-matter lesion (MCI−, normalized white-matter vascular lesions <4960) and MCI with high degree of white-matter lesion (MCI+,

normalized white-matter vascular lesions ≥4960)

Subjects (n) Gender (M/F) Age (years) MMSE IAF (Hz) White matter vascularlesion (voxels)

MCI+ 48 16/32 69.6 ± (1.2 SE) 26.5 ± (0.4 SE) 9.6 ± (0.2 SE) 7479 ± (483 SE)MCI− 48 16/32 70.1 ± (1.0 SE) 25.4 ± (0.4 SE) 9.3 ± (0.2 SE) 3332 ± (179 SE)AD+ 42 13/29 72.2 ± (1.2 SE) 21.0 ± (0.6 SE) 8.9 ± (0.2 SE) 8744 ± (1295 SE)AD− 41 12/29 70.6 ± (1.5 SE) 18.4 ± (0.8 SE) 8.7 ± (0.3 SE) 2298 ± (107 SE)

lowest vascular load or MCI− (<4960 voxels; mean273

of 3332 voxels ± 179 SE; n = 48). The same crite-274

rion based on the median of the white matter lesion275

was used to divide AD subjects into the sub-groups276

of AD+ (≥3430 voxels; mean of 8744 voxels ± 1295277

SE; n = 42) and AD− (<3430 voxels; mean of 2298278

voxels ± 107 SE; n = 41). Table 2 reports demographic279

and clinical data of the AD−, AD+, MCI−, and MCI+280

sub-groups.281

EEG recordings282

Resting state eyes closed EEG data were recorded283

(0.3–70 Hz bandpass) from 19 electrodes positioned284

according to the international 10–20 system (i.e. Fp1,285

Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3,286

Pz, P4, T6, O1, O2) and referenced to linked earlobes287

or cephalic reference. To monitor eye movements, the288

horizontal and vertical electrooculogram (0.3– 70 Hz289

bandpass) was simultaneously recorded. All data were290

digitized in continuous recording mode (about 5 min291

of EEG; 128–512 Hz sampling rate, the sampling rate292

being fixed in each recording research unit of this293

multi-centric study). In all subjects, EEG recordings294

were performed in the late morning. In order to keep295

constant the level of vigilance, an operator controlled296

on-line the subject and the EEG traces, verbally alert-297

ing the subject any time there were signs of behavioral298

and/or EEG drowsiness.299

Preliminary analysis of the EEG data300

The recorded EEG data were segmented and ana-301

lyzed off-line in consecutive 2 s epochs. The EEG302

epochs with ocular, muscular, and other types of arti-303

facts were preliminarily identified by a computerized304

automatic procedure. EEG epochs with sporadic blink-305

ing artifacts (less than 15% of the total) were then306

corrected by an autoregressive method [51]. Two inde-307

pendent experimenters – blind to the diagnosis at the308

time of the EEG analysis – manually confirmed the309

EEG segments accepted for further analysis. Finally, 310

we re-referenced artifact free EEG data to common 311

average for further analysis. 312

Spectral analysis of the EEG data 313

The digital FFT-based power spectrum analysis 314

(Welch technique, Hanning windowing function, no 315

phase shift) was evaluated in order to calculate the 316

individual alpha frequency (IAF) peak, defined as the 317

frequency associated to the strongest EEG power at 318

the extended alpha range of 6–13 Hz [53]. Mean IAF 319

peak was 9.3 Hz (±0.2 SE) in the Nold subjects, 9.5 Hz 320

(±0.1 SE) in the MCI subjects, and 8.8 Hz (±0.2 SE) in 321

the AD subjects. No statistically significant ANOVA 322

differences were found (p > 0.05). However, the IAF 323

peak was used as a covariate (together with age, gender, 324

recording unit site, and use or not of the ADNI protocol 325

in the statistics analyses. Indeed, the IAF is a frequency 326

of special importance, since it is associated with maxi- 327

mum power of resting eyes-closed EEG rhythms [52]. 328

The above procedure minimized the possibility that 329

small differences in the IAF peak could confound the 330

comparisons among the Nold, MCI, and AD groups. 331

The standard frequency bands of interest were delta 332

(2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), alpha 333

2 (10.5–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz) 334

and gamma (30–40 Hz), in continuity with a bulk of 335

reference previous studies on the cortical sources of 336

resting EEG rhythms in pathological aging [8, 14, 337

53–56]. Choice of the fixed EEG bands did not account 338

for IAF peak. However, this should not affect the 339

results, since more than 90% of the subjects had the 340

IAF peaks within the alpha 1 band (8–10.5 Hz) and the 341

IAF was used as a covariate in the statistical analysis. 342

Cortical source of EEG rhythms as computed 343

by LORETA 344

Low resolution electromagnetic source tomogra- 345

phy (LORETA) as provided at http://www.unizh. 346

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ch/keyinst/NewLORETA/LORETA01.htm was used347

for the estimation of cortical sources of EEG rhythms348

[57–59]. LORETA is a functional imaging technique349

belonging to a family of linear inverse solution proce-350

dures [60] modeling 3D distributions of EEG sources351

[59]. With respect to the dipole modeling of cortical352

sources, no a priori decision of the dipole position is353

required by LORETA procedure. LORETA belongs to354

the family of linear inverse algorithms like minimum355

norm solution, weighted minimum norm solution or356

weighted resolution optimization [58, 61, 62], and has357

been successfully used in recent EEG studies on patho-358

logical brain aging using the same experimental set359

up (electrode montage, sample frequency, etc.) of the360

present study [3, 7, 14, 53–56].361

LORETA computes 3D linear solutions (LORETA362

solutions) for the EEG inverse problem within a 3-shell363

spherical head model including scalp, skull, and brain364

compartments. The brain compartment is restricted to365

the cortical gray matter/hippocampus of a head model366

co-registered to the Talairach probability brain atlas367

and digitized at the Brain Imaging Center of the Mon-368

treal Neurological Institute [63]. This compartment369

includes 2394 voxels (7 mm resolution), each voxel370

containing an equivalent current dipole. Of note, EEG371

electrode positions were not co-registered to individ-372

ual brain source models; unfortunately, the official373

LORETA package did not include software to do so374

and we could not obtain the digitalization of the elec-375

trode position from our clinical units. LORETA can376

be used from EEG data recorded by low spatial sam-377

pling of 10–20 system (19 electrodes) when cortical378

sources are estimated from resting state eyes-closed379

EEG rhythms [1, 7, 14, 17, 23, 53–56, 64–71]. Indeed,380

resting state eyes-closed EEG rhythms are generated381

by coherent synchronous neural activity of large corti-382

cal areas (i.e., the summed activity of a large number383

of pyramidal neuron assemblies). As a result, these384

rhythms are characterized by low-spatial frequency385

content that can be properly sampled by the 19 scalp386

electrodes placed according to 10–20 system [72].387

LORETA solutions consisted of voxel z-current den-388

sity values able to predict EEG spectral power density389

at scalp electrodes, being a reference-free method of390

EEG analysis, in that one obtains the same LORETA391

source distribution for EEG data referenced to any392

reference electrode including common average. A nor-393

malization of the data was obtained by normalizing the394

LORETA current density at each voxel with the power395

density averaged across all frequencies (0.5–45 Hz)396

and across all 2394 voxels of the brain volume. After397

the normalization, the solutions lost the original physi-398

cal dimension and were represented by an arbitrary unit 399

scale. This procedure reduced inter-subjects variability 400

and was used in previous EEG studies [7, 14, 53–56]. 401

The general procedure fitted the LORETA solutions 402

in a Gaussian distribution and reduced inter-subject 403

variability [73, 74]. Other methods of normalization 404

using the principal component analysis are effective 405

for estimating the subjective global factor scale of the 406

EEG data [75]. These methods are not available in the 407

LORETA package, so they were not used in this study. 408

Solutions of the EEG inverse problem are under- 409

determined and ill conditioned when the number of 410

spatial samples (electrodes) is lower than that of the 411

unknown samples (current density at each voxel). 412

In order to properly address this problem, the corti- 413

cal LORETA solutions predicting scalp EEG spectral 414

power density were regularized to estimate distributed 415

rather than punctual EEG source patterns [57–59]. In 416

line with the low spatial resolution of the adopted tech- 417

nique, we used our MATLAB software to collapse all 418

voxels of LORETA solutions within each of the cortical 419

macroregions of interest (ROIs) such frontal, central, 420

parietal, occipital, temporal, and limbic regions of the 421

brain model. The belonging of a LORETA voxel to 422

a Brodmann area was defined by original LORETA 423

package. Table 3 lists the Brodmann areas (BAs) rep- 424

resented into each ROI. 425

A main advantage of the regional analysis of 426

LORETA solutions, using an explicit source model 427

coregistered into Talairach space, was that our mod- 428

eling could disentangle rhythms of contiguous cortical 429

areas (namely those from the occipital source were 430

disentangled with respect to those of the contiguous 431

parietal and temporal sources, etc). 432

Statistical analysis of the LORETA solutions 433

Statistical analysis aimed at evaluating two main 434

working hypotheses. These hypotheses were the fol- 435

lowing: (1) LORETA solutions of resting state cortical 436

Table 3Brodmann areas included in the cortical regions of interest (ROIs)of the present study. LORETA solutions were collapsed in frontal,

central, parietal, occipital, temporal, and limbic ROIs

LORETA Brodmann areas into the regionsof Interest (ROIs)

Frontal 8, 9, 10, 11, 44, 45, 46, 47Central 1, 2, 3, 4, 6Parietal 5, 7, 30, 39, 40, 43Temporal 20, 21, 22, 37, 38, 41, 42Occipital 17, 18, 19Limbic 31, 32, 33, 34, 35, 36

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EEG rhythms show difference in amplitude among the437

Nold, MCI, and AD subjects; (2) LORETA solutions438

point to difference in amplitude between AD+ and439

AD− groups as well as between MCI+ and MCI−440

groups. The LORETA solutions showing such signif-441

icant differences are correlated to the cognitive status442

as revealed by MMSE score.443

To test the first working hypothesis, the LORETA444

solutions values were used as a dependent variable445

for an ANOVA design using subjects’ age, gender,446

MMSE, IAF peak, and recording unit site as covari-447

ates. The ANOVA factors (levels) were Group (Nold,448

MCI, AD), Band (delta, theta, alpha 1, alpha 2, beta449

1, beta 2, gamma), and ROI (frontal, central, parietal,450

occipital, temporal, limbic). Mauchly’s test evaluated451

the sphericity assumption. Correction of the degrees of452

freedom was made with the Greenhouse-Geisser pro-453

cedure. Duncan test was used for post-hoc comparisons454

(p < 0.05). Specifically, the working hypothesis would455

be confirmed by a statistical ANOVA effect includ-456

ing the factor Group (p < 0.05), and planned post-hoc457

testing showing differences in line with the pattern458

Nold /= MCI /= AD (p < 0.05).459

To test the second working hypothesis, the LORETA460

solutions values were used as a dependent variable for461

an ANOVA design using subjects’ age, gender, MMSE,462

IAF peak, and recording unit site as covariates. The463

ANOVA factors (levels) were Group (MCI+, MCI−,464

AD+, AD−), Band (delta, theta, alpha 1, alpha 2, beta465

1, beta 2, gamma), and ROI (frontal, central, parietal,466

occipital, temporal, limbic). Mauchly’s test evaluated467

the sphericity assumption. Correction of the degrees468

of freedom was made with the Greenhouse-Geisser469

procedure. Duncan test was used for post-hoc com-470

parisons (p < 0.05). The working hypothesis would be471

confirmed by a statistical ANOVA effect including the472

factor Group (p < 0.05), and planned post-hoc testing473

showing differences between AD+ and AD− groups474

as well as between MCI+ and MCI− groups. Finally,475

EEG sources showing these statistically significant dif-476

ferences as a function of the white matter vascular477

lesions were correlated to MMSE score in the con-478

tinuum of the MCI and AD subjects as a whole group479

(Pearson test, p < 0.05).480

Novelty of the present study481

This study is a part of larger scientific program on482

EEG markers of AD, yet it is well framed and dis-483

tinct from the previous studies of the Authors [7, 14,484

53–56]. Specifically, this is our first study examining485

the relationships between resting state EEG sources486

and white matter vascular lesions in AD. To address 487

this issue, we performed an unedited analysis of white 488

matter vascular lesions by EMS-SPM software in 83 489

AD patients and 96 amnesic MCI subjects, for the com- 490

parison of EEG sources between sub-groups of these 491

subjects with a different degree of white matter vascu- 492

lar lesion. Among these subjects, no AD patient and 64 493

amnesic MCI subjects had been previously used in the 494

reference investigations evaluating white matter vascu- 495

lar lesions by Wahlund visual rating scale [1, 23]. The 496

results of the present analysis are absolutely original 497

(i.e., never published before). 498

RESULTS 499

Figure 1 shows the grand average of regional 500

normalized LORETA solutions (i.e., relative power 501

current density averaged with each ROI)) relative to an 502

ANOVA interaction (F(60,6480) = 11.91; p < 0.0001) 503

among the factors Group (Nold, MCI, AD), Band 504

(delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma), 505

and ROI (frontal, central, parietal, occipital, tempo- 506

ral, limbic). Planned post-hoc testing indicated that 507

occipital alpha2 sources as well as parietal, occip- 508

ital, temporal, and limbic alpha 1 sources were 509

higher in amplitude in the Nold than MCI group 510

(p < 0.000005), and in the MCI than AD group 511

(p < 0.000005 to 0.000001); these results disclosed 512

the pattern Nold > MCI > AD for the parietal, occip- 513

ital, temporal, and limbic alpha 1 and occipital alpha2 514

sources. Furthermore, frontal, temporal, and limbic 515

delta sources were lower in amplitude in the Nold and 516

MCI than in the AD groups (p < 0.05). 517

Figure 2 maps the grand average of the normalized 518

LORETA solutions (i.e., relative power current den- 519

sity) modeling the distributed cortical EEG sources 520

for delta, theta, alpha 1, alpha 2, beta 1, beta 2, and 521

gamma bands in the AD−, AD+, MCI−, MCI+ groups. 522

Posterior alpha sources were generally higher in ampli- 523

tude in the AD+ or MCI+ than AD− or MCI− group, 524

whereas the opposite is true for the posterior delta 525

sources. 526

Figure 3 plots the grand average of regional normal- 527

ized LORETA solutions (i.e., relative power current 528

density averaged with each ROI) relative to an ANOVA 529

interaction (F(90,5250) = 3.50; p < 0.00001) among 530

the factors Group (AD−, AD+, MCI−, MCI+), Band 531

(delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma), 532

and ROI (frontal, central, parietal, occipital, tempo- 533

ral, limbic). Planned post-hoc testing indicated that 534

occipital, temporal, and limbic alpha 1 sources were 535

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Fig. 1. Statistical ANOVA interaction (F(60,6480) = 11.91; p < 0.0001) among the factors Group (Nold, MCI, AD), Band (delta, theta, alpha 1,alpha 2, beta 1, beta 2, gamma), and ROI (frontal, central, parietal, occipital, temporal, limbic).

higher in amplitude in the MCI+ than MCI− group536

(p < 0.01 to 0.000001). Furthermore, occipital delta537

sources were higher in amplitude in the AD− than538

AD+ group (p < 0.01). Finally, central, parietal, occip-539

ital, temporal, and limbic alpha sources were higher540

in amplitude in the AD+ than AD− group (p < 0.05 to541

0.000001).542

The mentioned delta and alpha sources showing543

statistically significant differences (p < 0.05) as a func-544

tion of the white matter vascular lesions (i.e., MCI+545

or AD+ versus MCI− or AD−) were correlated546

to MMSE score in the continuum of the MCI and547

AD subjects as a whole group. There was a posi-548

tive correlation between MMSE score and any of the549

mentioned alpha sources at central (alpha 1, r = 0.18,550

p = 0.02; alpha 2, r = 0.20, p = 0.008), parietal (alpha551

1, r = 0.28, p = 0.0001; alpha 2, r = 0.32, p = 0.0001),552

occipital (alpha 1, r = 0.25, p = 0.001; alpha 2, r = 0.27,553

p = 0.0001), temporal (alpha 1, r = 0.31, p = 0.0001;554

alpha 2, r = 0.26, p = 0.001), and limbic (alpha 1, 555

r = 0.30, p = 0.0001; alpha 2, r = 0.30, p = 0.0001) 556

macroregions. The higher the MMSE score, the higher 557

the amplitude of alpha sources. Furthermore, there was 558

a negative correlation between MMSE score and occip- 559

ital delta sources (r = −0.31, p = 0.0001). The lower 560

the MMSE score, the higher the amplitude of occipital 561

delta sources.562

Control analyses 563

We performed some control analyses to ascertain if 564

the results of the main statistical analysis were affected 565

by relevant confounding variables. 566

In a first control analysis, we tested whether the 567

statistical results were influenced by the presence of 568

ADNI and non-ADNI subjects in the MCI and AD 569

groups. We divided the MCI and AD group in ADNI 570

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Fig. 2. Grand average of LORETA solutions (i.e., normalized relative current density at the cortical voxels) modeling the distributed EEGsources for delta, theta, alpha 1, alpha 2, beta 1, beta 2, and gamma bands in Nold, MCI – (normalized white-matter vascular lesions < 4960),MCI+(normalized white-matter vascular lesions ≥4960), AD – (normalized white-matter vascular lesions < 3430), and AD+(normalized white-matter vascular lesions ≥3430) groups. The left side of the maps (top view) corresponds to the left hemisphere. Legend: LORETA, low resolutionbrain electromagnetic tomography. Color scale: all power density estimates were scaled based on the averaged maximum value (i.e., alpha 1power value of occipital region in Nold).

(18 MCI and 50 AD) and non-ADNI (78 MCI and571

33 AD). Statistical analysis of the LORETA source572

solutions showed no statistically significant difference573

(F(1.91) = 0.01; p < 0.9235) between MCI ADNI and574

MCI non-ADNI sub-groups. The same was true in575

AD patients, namely no statistically significant dif-576

ference (F(1.78) = 0.49; p < 0.4839) between the AD577

ADNI and AD non-ADNI sub-groups.578

In a second control analysis, we compared the Nold,579

MCI, and AD groups matched as number (40 AD,580

40 MCI, and 40 Nold subjects), mean age (AD = 69.5581

years; MCI = 69.8 years; Nold = 72.1 years), and mean582

IAF (AD = 8.6, MCI = 9.3, and Nold = 9.3 hertz). This 583

allowed a good control of the inter-groups variabil- 584

ity. The ANOVA design and covariates were those of 585

the main ANOVA design. The ANOVA factors were 586

Group (Nold, MCI, AD), Band (delta, theta, alpha 1, 587

alpha 2, beta 1, beta 2, gamma), and ROI (frontal, cen- 588

tral, parietal, occipital, temporal, limbic). The results 589

showed a statistically significant interaction among all 590

factors (F(60,3510) = 14.17; p < 0.0001). As expected, 591

ANOVA showed the well known abnormalities of delta 592

and alpha sources, in detail the results disclosed the pat- 593

tern Nold > MCI > AD for the parietal, occipital, and 594

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Fig. 3. Statistical ANOVA interaction (F(90,5250) = 3.50; p < 0.00001) among the factors Group (AD−, AD+, MCI−, MCI+), Band (delta,theta, alpha 1, alpha 2, beta 1, beta 2, gamma), and ROI (frontal, central, parietal, occipital, temporal, limbic).

limbic alpha and temporal alpha1 sources. Further-595

more, frontal, occipital, and temporal delta sources596

were lower in amplitude in the Nold and MCI than597

in the AD groups (p < 0.05). These results globally598

confirmed those of the main analysis.599

In a third control analysis, we compared white matter600

vascular lesions between MCI+ versus MCI− groups601

as well as between AD+ versus AD− groups. As602

expected, ANOVA showed that there were significantly603

higher values of the white matter vascular lesions in the604

MCI+ than MCI− (p < 0.00001) as well as in the AD+605

than AD− subjects (p < 0.00001).606

A fourth control analysis accounted for the variabil-607

ity of the MMSE score across the groups, although608

the MMSE score was used as a covariate in the main609

ANOVA design. We selected sub-groups of AD−,610

AD+, MCI−, and MCI+ subjects to minimize the dif-611

ference of MMSE score between the AD− and AD+612

groups as well as between the MCI− and MCI+groups613

(Table 4). The ANOVA design and covariates were614

those of the main ANOVA design. The ANOVA factors 615

were Group (AD−, AD+, MCI−, MCI+; independent 616

variable), Band (delta, theta, alpha 1, alpha 2, beta 1, 617

beta 2, gamma), and ROI (central, frontal, parietal, 618

occipital, temporal, limbic). The results showed a 619

statistically significant interaction among all factors 620

(F(90,2700) = 2.81; p < 0.00001). Planned post-hoc 621

testing indicated that central and temporal alpha 1 622

sources were higher in amplitude in the MCI+ than 623

MCI− group (p < 0.05). Furthermore, occipital delta 624

sources were lower in amplitude in the AD+ than AD− 625

group (p < 0.05). Finally, central, parietal, occipital, 626

and temporal alpha 1 sources were higher in amplitude 627

in the AD+than AD− group (p < 0.01). These results 628

globally confirmed those of the main ANOVA design. 629

DISCUSSION 630

In the present study, we tested the novel hypothesis 631

that in AD subjects, resting state closed-eye EEG 632

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Table 4Demographic and clinical data of the subjects’ sub-groups created to minimize the difference of MMSE between AD− and AD+ as well as

between MCI− and MCI+

Subjects (n) Gender (M/F) Age (years) MMSE IAF (Hz) White matter vascularlesion (voxels)

MCI+ 28 12/21 68.3 ± (1.5 SE) 26.3 ± (0.3 SE) 9.6 ± (0.2 SE) 7800 ± (713 SE)MCI− 28 13/20 69.0 ± (1.1 SE) 26.2 ± (0.3 SE) 9.5 ± (0.2 SE) 2973 ± (227 SE)AD+ 19 8/11 72.1 ± (1.8 SE) 19.5 ± (0.8 SE) 8.8 ± (0.3 SE) 9795 ± (2403 SE)AD− 19 7/12 70.1 ± (2.7 SE) 19.6 ± (0.8 SE) 8.4 ± (0.4 SE) 2231 ± (200 SE)

rhythms are not deteriorated due to the amount of633

white-matter vascular lesion, thus extending previous634

evidence in amnesic MCI subjects [1, 23]. To address635

this hypothesis, we estimated white matter vascular636

lesion with friendly and automated software (i.e., EMS637

tool of SPM) towards possible clinical applications.638

Such procedure was based on the use of PD, T1-,639

and T2-weighted MRIs recorded in amnesic MCI640

and AD subjects. Of note, we collected about 42%641

of the MRIs following the standards of the ADNI642

project (http://www.adni-info.org/), which aims at643

standardizing neuroimaging exams of aged people in644

multicenter AD studies on new markers and drugs.645

In this vein, we estimated cortical sources of resting646

state EEG rhythms by LORETA software, which can647

be freely downloaded by Internet (http://www.unizh.648

ch/keyinst/NewLORETA/LORETA01.htm), and has649

been successfully used by our Consortium in several650

field investigations [7, 14, 53–56].651

In the present study, results of the control statisti-652

cal analysis confirmed that the amnesic MCI subjects653

showed a decrease in amplitude of low-frequency alpha654

sources (8–10.5 Hz) compared to the Nold subjects.655

The AD subjects were characterized by an amplitude656

increase of delta sources (2–4 Hz), along with a strong657

amplitude reduction of low-frequency alpha sources.658

These findings are globally in line with previous evi-659

dence showing a pathological enhancement of the delta660

rhythms in AD subjects [7, 8, 76, 77], and a magnitude661

decrease of default alpha rhythms in MCI and/or AD662

subjects [2, 3, 7, 8, 15, 21, 22, 78]. Therefore, these663

control findings validated the present procedures for664

subjects’ selection and EEG data analysis, thus corrob-665

orating the novel results on the relationships between666

resting state EEG sources and white matter vascular667

lesions in AD subjects.668

Results of the main statistical analysis indicated that669

amplitude of posterior low-frequency alpha sources670

was higher in the amnesic MCI+ than MCI− group.671

As a novel finding, amplitude of occipital delta sources672

was lower in the AD+ than AD− group, whereas the673

opposite was true for central and posterior low- and 674

high-frequency alpha sources. These results suggest 675

that in AD subjects, central and posterior resting state 676

delta and alpha rhythms are not deteriorated with the 677

increase of white-matter vascular lesion, thus extend- 678

ing previous evidence on alpha sources in amnesic MCI 679

subjects [1, 23]. 680

Why were resting state EEG rhythms not deterio- 681

rated by the increase of white matter vascular lesions 682

in amnesic MCI and AD subjects? To answer to this 683

question, a brief overview on “normal” delta and 684

alpha rhythms is helpful. In the condition of slow- 685

wave sleep, corticofugal slow oscillations (<1 Hz) are 686

effective in grouping thalamic-generated delta rhythms 687

(1–4 Hz) and spindling activity (7–14 Hz) rhythms 688

[79]. In the condition of brain arousal, spindles as 689

well as high and low-frequency components of the 690

delta rhythms are blocked by the inhibition of oscilla- 691

tors within, respectively, reticulo-thalamic (7–14 Hz), 692

thalamo-cortical (1–4 Hz), and intracortical (<1 Hz) 693

neuronal circuits. These rhythms are replaced by fast 694

(beta and gamma) cortical oscillations, which are 695

mainly induced by forebrain (nucleus basalis) cholin- 696

ergic inputs to hippocampus and cortex as well as 697

by thalamocortical projections [79, 80]. In the con- 698

dition of awake rest, low-frequency (8–10.5 Hz) alpha 699

would be mainly related to subject’s global attentional 700

readiness [72, 81–84] and would mainly reflect time- 701

varying inputs of cortico-cortical and thalamo-cortical 702

pathways [85]. Noteworthy, there is consensus that 703

alpha rhythms represent the dominant resting oscil- 704

lations of the adult, awake human brain [72, 81–84], 705

and have been linked to intelligent quotient, memory, 706

and cognition [52]. Keeping in mind this physio- 707

logical premise, loss of synapses and neurons along 708

the well known tracks of AD neurodegeneration [86, 709

87] may deteriorate the synchronization of cortical 710

pyramidal neurons generating default alpha rhythms, 711

and may disinhibit pathological delta rhythms in the 712

condition of resting state. In this framework, diffuse 713

white matter vascular lesions may not specifically 714

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12 C. Babiloni et al. / EEG and Vascular Lesion in AD

impair the neural circuits responsible for the transfer715

of signals into brain pathways that generate resting716

state alpha rhythms and inhibit pathological delta717

rhythms.718

The present results support the notion that cere-719

brovascular and AD lesions do not represent additive720

or synergistic factors in the determination of the rest-721

ing state EEG abnormalities during the evolution of722

the disease, although these lesions contribute to the723

development of cognitive impairment in AD patients724

[24, 26, 88]. In AD, cognitive and clinical conditions725

are affected by the severity of both neurodegener-726

ative and cerebrovascular lesions in hippocampal,727

anterior cingulate gyrus, and parieto-temporal regions728

[89, 90–94]. Furthermore, these conditions depend on729

amyloid angiopathy of small vessels and on their struc-730

ture/function [27–29]. Current evidence suggests that731

there is decreased vascular density in aging and AD,732

with a cerebrovascular dysfunction that precedes and733

accompanies cognitive dysfunction and neurodegener-734

ation [95]. A decline in cerebrovascular angiogenesis735

typically inhibits recovery from hypoxia-induced cap-736

illary loss and cerebral blood flow may be inhibited by737

tortuous arterioles and deposition of excessive collagen738

in veins and venules [96]. In this framework, hypoper-739

fusion may occurs early in AD, inducing white matter740

lesions and correlating with dementia [95–99]. How-741

ever, resting state EEG abnormalities would be mainly742

affected by the AD neurodegenerative impairment of743

brain circuits, which may be not specifically targeted744

by diffuse white matter vascular lesions. Therefore, it745

can be speculated that resting state EEG rhythms might746

be more sensitive to neurodegenerative processes than747

cerebrovascular lesions in AD.748

In conclusion, we tested whether cortical synchro-749

nization mechanisms at the basis of resting state EEG750

rhythms are abnormal in AD subjects, as a function751

of vascular lesion of white matter. The present results752

showed that in both amnesic MCI and AD subjects,753

posterior delta and alpha sources did not deteriorate754

with the increase of white matter vascular lesion,755

although white matter is known to be impaired along756

AD neurodegenerative process. These results need to757

be validated with a follow-up study evaluating resting758

state EEG rhythms and white matter vascular lesion. In759

principle, the present results suggest that abnormalities760

of resting state EEG rhythms might be related to AD761

neurodegeneration specifically impinging on the brain762

circuits generating these rhythms and cognitive sta-763

tus rather than to white matter vascular lesion globally764

affecting the whole brain.765

ACKNOWLEDGMENTS 766

We thank Dr. Paul Suetens of Medical Image Com- 767

puting Group at KU Leuven, for providing software 768

EMS used for MRI data analysis. We also thank 769

Prof/Drs. Carla Buttinelli, Brunello Lecce, Anna- 770

maria Papantonio, Paolo Tisei, Antonella De Carolis, 771

Silvia Guidoni, Teresa Falco, Daniela Buonanno, 772

Manuela De Stefano, Federica Scrascia, Livia Quin- 773

tiliani, Simone Migliore, Daniela Cologno, Loreto 774

Gesualdo, Elena Ranieri, Ivan Cincione, Antonello 775

Bellomo, Annamaria Petito, Mario Altamura, Pietro 776

Fiore, Andrea Santamato, Tommaso Cassano, Dario 777

Colella, Giuseppe Cibelli, and Giancarlo Rossi-Fedele 778

for their excellent clinical, biometrics, tecnica, and 779

data analysis work. This research was developed 780

thank to the financial support of Tosinvest Sanita’ 781

(Cassino, Pisana) and Italian Ministry of Health 782

(Strategic research project entitled “Diagnosis of incip- 783

ient Alzheimer disease”) for the collection but not for 784

the analysis of the data reported in this manuscript. 785

The research leading to the present results has also 786

received funding from the European Community’s 787

Seventh Framework Programme (FP7/2007–2013) for 788

the Innovative Medicine Initiative under Grant Agree- 789

ment no 115009 (Prediction of cognitive properties 790

of new drug candidates for neurodegenerative dis- 791

eases in early clinical development, Pharmacog). It 792

was conducted as part of the analysis of “historical” 793

(archive) EEG and MRI data in control, amnesic MCI, 794

and AD subjects performed by two units of the Phar- 795

maCog Consortium, namely the University of Foggia 796

(Prof./Dr. Claudio Babiloni, Loreto Gesualdo, Elena 797

Ranieri, Ivan Cincione, Antonello Bellomo, Anna- 798

maria Petito, Mario Altamura, Pietro Fiore, Andrea 799

Santamato, Tommaso Cassano, Dario Colella, and 800

Gaetano Serviddio) and IRCCS Fatebenefratelli of 801

Brescia (Dr. Giovanni B. Frisoni, Marina Boccardi, 802

and Alberto Redolfi). PharmaCog funding was used 803

to support the analysis but not the collection of the 804

data reported in this manuscript. After a gentleman 805

agreement among all participants to this research, 806

only Prof./Dr. Claudio Babiloni, Fabrizio Vecchio, 807

Giovanni B. Frisoni, Marina Boccardi, and Alberto 808

Redolfi represented the PharmaCog Consortium in the 809

Author list. We thank Prof./Drs. Elaine irving, Gian- 810

luigi Forloni, Francesco Mattia Noe’, Tilman Hensch, 811

Oscar Dalla Pasqua, David Bartres-Faz, David Wille, 812

Giuseppe Bertini, and Paolo Fabene for a fruitful scien- 813

tific discussion of the results in the framework of the 814

PharmaCog Consortium. For further information on 815

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the PharmaCog project please refer to www.alzheimer-816

europe.org.817

Authors’ disclosures available online (http://www.818

j-alz.com/disclosures/view.php?id=847).819

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