DECIDE EEG facilities FOR TRAINING AND DISSEMINATION of SCIENTIST profile: The basis of the EEG science for Alzheimer’s disease

ISOLATED CORTEX Reticular neurons Relay neurons Spontaneous delta rhythms of cerebral cortex when disconnected from cortical and sub-cortical inputs pyramidal neurons oscillating at synchronized delta frequencies (around 1 Hz) =All neurons synchronized at around 1 Hz Reticular neurons Relay neurons BRAIN STEM THALAMUS

RESTING EYES CLOSED Reticular neurons Relay neurons Dominant resting (eyes-closed) alpha rhythms are synchronous and coherent over wide cortical areas and corresponding thalamic nuclei pyramidal neurons oscillating at synchronized alpha frequencies (around 10 Hz) =All neurons synchronized at around 10 Hz Reticular neurons Relay neurons BRAIN STEM THALAMUS

EVENT Reticular neurons Relay neurons BRAIN STEM THALAMUS Gamma rhythms EVENT High-frequency EEG rhythms (20 to 100 Hz orhighest) substitute alpha during eyes opening. These rhythms are coherent over small cortical areas and corresponding thalamic nuclei, and different sub-populations show different frequencies for opening their communication channel. pyramidal neurons oscillating at several peculiar high frequencies (beta-gamma) = synchronous at around 20 Hz Reticular neurons Relay neurons = synchronous at around 40 Hz = synchronous at around 100 Hz BRAIN STEM THALAMUS

Sources of on-going alpha rhythms in visual cortex (dog) Cortical deep profile shows a polarity reversal across cortical layers reflecting a dipolar field. Lopes da Silva, and Storm van Leeuwen, 1977. Courtesy by Dr. F. Lopes da Silva

Cortical alpha rhythms are negatively correlated with hemodynamic signals (BOLD) in parietal and occipital cortex Cortical alpha rhythms are positively correlated with hemodynamic signals in thalamus 1, The ARF of the Thalamus is biphasic and initially positive; 2. ARFs of occipital and parietal areas are negative. 3. The peak time of the Thalamic ARF is earlier than that of the cortex. 1, The ARF of the Thalamus is biphasic and initially positive; 2. ARFs of occipital and parietal areas are negative. 3. The peak time of the Thalamic ARF is earlier than that of the cortex. De Munck, Gonçalves, Huijboom, Kuijer, Pouwels, Heethaar, Lopes da Silva, NeuroImage 2007, 35: 1142 – 1151. (Courtesy by Dr- F. Lopes da Silva) FDR < 0.001

APPLICATION OF EEG MARKERS TO ALZHEIMER’S DISEASE

Mild cognitive impairment (MCI) APPLICATION TO CLINICAL NEUROPHYSIOLOGY Which qEEG markers for early diagnosis, prognosis, and monitoring of Alzheimer disease? Normal elderly (Nold) Mild cognitive impairment (MCI) AD

Basic methodology: 10-20 electrode montage and LORETA for source analysis of resting eyes-closed EEG Resting eyes closed (2 min), eyes open (2 min) LORETA 10-20 electrode system LORETA solutions averaged with cortical lobes (frontal, central, parietal, temporal, occipital, limbic) Psychometric testing and neurological evaluation

A new approach to LORETA analysis: MACROREGIONS based on Brodmann areas Frontal (areas) 8, 9, 10, 11, 44, 45, 46, 47 Central 1, 2, 3, 4, 6 Parietal 5, 7, 30, 39, 40, 43 Temporal 20, 21, 22, 37, 38, 41, 42 Occipital 17, 18, 19 Limbic 12, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36 Regions of interest (ROIs)

qEEG markers of physiological aging: cortical resting EEG rhythms characterizing normal elderly (Nold) subjects compared to normal young subjects (physiological aging)

Physiological aging Diagnosis: DSM-IV and NINCDS-ADRDA criteria Nyoung Nold N 108 107 Age (years) 27.3 (±7.3SD) 67.3 (±9.2 SD) Gender (F/M) 56/52 67/40 MMSE 30 28.5 (±1.2 SD) Education (years) 15.9 (±2.6 SD) 9.6 (±4.2 SD) Diagnosis: DSM-IV and NINCDS-ADRDA criteria EEG data: 5 min of resting EEG (closed eyes) Data analysis: artifact rejection, LORETA at ROIs, statistical analysis (age, MMSE, IAF, and education as covariates)

Posterior sources of resting alpha rhythms were lower in power in normal elderly than young subjects, despite similar degree of global cognition. Resting EEG data Babiloni C, Binetti G, Cassarino A, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Frisoni G, Galderisi S, Hirata K, Lanuzza B, Miniussi C, Mucci A, Nobili F, Rodriguez G, Romani GL, and Rossini PM. Sources of cortical rhythms in adults during physiological aging: a multi-centric EEG study. Human Brain Mapping 2006 Feb;27(2):162-72..

qEEG markers for differential diagnosis: cortical resting EEG rhythms characterizing mild AD compared to cerebrovascular dementia (VaD) and Parkinson disease with dementia

Posterior sources of resting alpha rhythms were lower in power in mild AD than VaD subjects, despite similar degree of global cognition. Resting EEG data: 38 Nold 48 mild AD 20 VaD Babiloni C, Binetti G, Cassetta E, Cerboneschi D, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Lanuzza B, Miniussi C, Moretti DV, Nobili F, Pascual-Marqui RD, Rodriguez G, Romani GL, Salinari S, Tecchio F, Vitali P, Zanetti O, Zappasodi F, Rossini PM. Mapping distributed sources of cortical rhythms in mild Alzheimer's disease. A multicentric EEG study. Neuroimage. 2004; 22(1): 57-67.

Posterior sources of resting alpha rhythms were lower in power in mild AD than PDD subjects but the opposite was true for widespread theta rhythms Resting EEG data: 20 Nold 13 PDD 20 mild AD Babiloni Claudio, De Pandis Francesca, Vecchio Fabrizio, Buffo Paola, Sorpresi Fabiola, Frisoni Giovanni B. and Rossini Paolo M. Cortical sources of resting state electroencephalographic rhythms in Parkinson’s disease related dementia and Alzheimer’s disease (Clinical Neurophysiology, 2011)

qEEG markers for preclinical diagnosis of AD: cortical resting EEG rhythms characterizing mild cognitive impairment (MCI) and subjective memory complaint (SMC)

Resting EEG data: 126 Nold 155 MCI 193 mild AD Posterior sources of resting delta and alpha rhythms gradually change in amplitude along Nold, MCI, and mild AD continuum Resting EEG data: 126 Nold 155 MCI 193 mild AD Babiloni C, Binetti G, Cassetta E, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Frisoni G, Hirata K, Lanuzza B, Miniussi C, Moretti DV, Nobili F, Rodriguez G, Romani GL, Salinari S, and Rossini PM Sources of cortical rhythms in subjects with mild cognitive impairment: a multi-centric study Clinical Neurophysiology 2006

Posterior sources of resting alpha rhythms are higher in amplitude in the Nold than in the SMC and MCI subjects, and in the amnesic than in the non amnesic MCI Resting EEG data: 74 Nold (Normal elderly) 29 SMC (Subjective Memory Complaint) 30 naMCI (Non Amnesic MCI) 57 aMCI (Amnesic MCI) C Babiloni; PJ Visser, G Frisoni, D Colombo, PP De Deyn, L Bresciani, V Jelic, G Nagels, G Rodriguez, PM Rossini, F Vecchio, F Verhey, LO Wahlund, F Nobili. Cortical sources of resting EEG rhythms in mild cognitive impairment and subjective memory complaint. Neurobiology of Aging 2009 NETWORK OF EXCELLENCE “DESCRIPA”

qEEG markers related to AD neurodegeneration: cortical resting EEG rhythms associated to structural MRI (atrophy, vascular lesion) markers in MCI and AD subjects

Posterior sources of resting alpha rhythms gradually change in amplitude along MCI and mild AD continuum as a function of hippocampal atrophy Resting EEG data: 40 MCI + hippocampal volume (+h) - hippocampal volume (-h) 35 mild AD Babiloni C, Frisoni GB, Pievani M, Vecchio F, Lizio R, Geroldi C, Fracassi C, Eusebi F, and Rossini PM. Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease. Neuroimage 2009

Babiloni C, Carducci F, Lizio R, Vecchio F, Baglieri A, Bernardini S, Boccardi M, Bozzao A, Buttinelli C, Esposito F, Giubilei F, Guizzaro A, Marino S, Montella P, Quattrocchi C, Redolfi A, Soricelli A, Tedeschi G, Triggiani I, Rossi-Fedele G, Parisi L, Vernieri F, Rossini PM, and Frisoni GB- Resting state cortical electroencephalographic rhythms are related to gray matter volume in subjects with mild cognitive impairment and Alzheimer’s disease: an ADNI project. Human Brain Mapping (under revision)

qEEG markers for therapy monitoring and drug discovery in AD: cortical resting EEG rhythms characterizing response to Donepezil and Ibuprofen

Resting EEG data: 28 Non Responder 30 Responder Long-term (1 year) cholinergic therapy reduces (i.e. it does not stop) the decline of occipital-temporal alpha sources in Alzheimer Responders when compared to Non-responders. Graphs illustrate the power of the EEG sources at baseline (before the therapy) minus follow up Resting EEG data: 28 Non Responder 30 Responder Babiloni C, Cassetta E, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Lanuzza B, Miniussi C, Moretti DV, Nobili F, Pascual-Marqui R, Rodriguez G, Romani GL, Salinari S, Zanetti O, and Rossini PM. Donepezil effects on sources of cortical rhythms in mild alzheimer’s disease: responders vs. non responders. NeuroImage 2006

FUNCTIONAL COUPLING OF EEG RHYTHMS

Both should be considered Neural networks integrate their activity by linear and non-linear functional coupling of EEG rhythms Linear coupling Both should be considered Non-linear coupling

Linear temporal synchronization (coherence) of EEG rhythms at electrode pairs as an index of functional cortico-cortical coupling (information transfer) brain max coh = 1 frontal frontal EEG high spectral coherence = high information transfer electrodes parietal EEG parietal linear coupling Per determinare la connessione funzionale tra le aree parietali e frontali ho applicato la coerenza spettrale alle coppie elettrodiche F3-F4, P3-P4, F3-P3, F4-P4. La coerenza è una misura della stabilità dello sfasamento tra le componenti ad una fissata frequenza di due segnali registrati ha valori compresi tra 0 e 1, per uno sfasamento costante si ha un valore unitario e quindi massimo della coerenza. Ne consegue (per segnali biologici) che la SC è una misura di connettività funzionale dei generatori di segnali elettrofisiologici.

Resting EEG data: 33 Nold 52 MCI 47 AD Babiloni Claudio, Frisoni Giovanni B, Vecchio Fabrizio, Pievani Michela, Geroldi Cristina, De Carli Charles, Ferri Raffaele, Lizio Roberta, and Rossini Paolo M. Global functional coupling of resting EEG rhythms is abnormal in mild cognitive impairment and alzheimer’s disease: a multicentric EEG study. Journal of Psychophysiology

MVAR model estimates “direction” of information flow by DTF “Directionality” (directed transfer function, DTF) of EEG rhythms at electrode pairs reflects fluxes of information within cortico-cortical coupling Frontal Parietal Il modello autoregressivo multivariato viene usato per stimare la DTF. Nell’MVAR X è il vettore che rappresenta i segnali, E rumore bianco, A la matrice dei coefficienti del modello dalla quale si arriva alla DTF. La DTF è un valore normalizzato e restituisce valori compresi tra 0 e 1. MVAR model estimates “direction” of information flow by DTF Kaminski MJ, Blinowska KJ. A new method of the description of the information flow in the brain structures. Biol Cybern. 1991;65(3):203-10.

Parietal to frontal direction of the information flux within EEG functional coupling (DTF) was stronger in Nold than in MCI and/or AD subjects Resting EEG data: 64 Nold 67 MCI 73 mild AD Claudio Babiloni, Raffaele Ferri, Giuliano Binetti, Fabrizio Vecchio, Giovanni B. Frisoni, Bartolo Lanuzza, Carlo Miniussi, Flavio Nobili, Guido Rodriguez, Francesco Rundo, Andrea Cassarino, Francesco Infarinato, Emanuele Cassetta, Serenella Salinari, Fabrizio Eusebi, and Paolo M. Rossini, Directionality of EEG synchronization in Alzheimer's disease subjects. Neurobiology of aging, 2007

DECIDE EEG facilities FOR TRAINING AND DISSEMINATION: Introduction to the features of EEG data and software in the e-infrastructure

DECIDE Consortium Public Private Start date: 9/1/2010 Small and Medium Enterprises (SMEs): IRCCS Fatebenefratelli Brescia, Italy IRCCS SDNi Naples, Italy MAAT G, Gevneve, Ch Patient Group: Alzheimer Europe Academic Institutions: GARR (Co-coordinator), Rome University of Milan Vita-Salute San Raffaele, Italy CNR of Milan, Italy University of Foggia, Italy University of Genova, Italy University of Warrsaw, Poland Imperial College, London UK Centre hospitalier universitaire de Toulouse, Toulouse - France Start date: 9/1/2010 Duration: 30 months Total cost: € 2.4 M €

EEG-EOG-EKG montages Features of the selected resting state EEG-EOG-EKG datasets EKG (V6) EOGs EEG montage

Preprocessing artifact detection Statistics & send report User: Clinician – All parameters fixed Scientist – Several parameters can be optimized GridDATALOAD GRidDTF/COHERENCE GridEEGQUALITY GridEEGSTAT θ α reference EEG databases (accepting EEG epochs) MCI Nold Alzh. ϐ γ Model order, Frequency resolution What diagnosis? GRidEEGSOURCE nameEEGreport.pdf (rejecting EEG epochs) θ α Z score Nold area γ ϐ AD axis 0=AD nameEEGtrials.txt AD area (2-sec EEG epochs in ASCII format) 0 Z score Nold axis 0=Nold Data upload Preprocessing artifact detection Window, Frequency resolution,… Statistics & send report Processing & markers

Testing and validation Developement and testing of an advanced variant of GridEEGDTF able to estimate DTF from 2-sec EEG segments Validation of the EEG algorithms on the uploaded reference population EEG databases (Nold, MCI, AD) Use of the EEG algorithms by Neurologists and collegues in the framework of the diagnostic service

Accuracy of benchmark EEG markers: mean Mahlanobis distance Bi-dimensional graph showing the distribution of all 82 Nold and 96 AD subjects of the Mahalanobis distance to the reference Nold and AD populations. The Mahalanobis distance was computed with reference to the mean values of the Nold (vertical axis) and AD (horizontal axis) populations within the 4-dimension space, namely 2 dimensions for the EEG spectral coherence (Fz-Pz at delta and alpha 1) and 2 dimensions for the LORETA source power (occipital delta and alpha 1). Ideally, the higher the Mahalanobis distance to a given population, the lower the probability to belong to that population. The Nold subjects are denoted by red squares, while the AD are denoted by blue circles.

Accuracy of benchmark EEG markers: results Results about classification performance of the mentioned 4 EEG variables showed 80.2% of mean sensibility, 61.8% of mean specificity, and 71.8% of mean accuracy over 100,000 classification rounds using “training” (80%) and “testing” (20%) sub-populations of AD and Nold datasets. Mean ROC (receiver operating characteristic) curve illustrating the performance of the classifier using Mahalanobis distance of the individual EEG datasets from Nold and AD population reference values. Area under curve (AUC) was of 0.78 (moderate classification performance; Swets, 1998). True positive rate indicates the probability of the correct classification of AD datasets (sensitivity), whereas false positive rate indicates the probability of the correct classification of Nold datasets (specificity). The horizontal axis indicates 1-false positive cases. Optimum values of the cut off optimizing sensitivity and specificity is plotted (circle).

CONCLUSIONS