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

2. 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

3. 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

4. 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

5. 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

6. 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 – (Courtesy by Dr- F. Lopes da Silva)
FDR < 0.001

7. APPLICATION OF EEG MARKERS TO ALZHEIMER’S DISEASE

8. 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

9. 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

10. 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)

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

12. 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)

13. 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):

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

15. 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):

16. 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)

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

18. 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

19. 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”

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

21. 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

22. 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)

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

24. 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

25. FUNCTIONAL COUPLING OF EEG RHYTHMS

26. 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

27. 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.

28. 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

29. 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):

30. 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

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

32. 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: months
Total cost: € 2.4 M €

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

34. 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

35. 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

36. 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.

37. 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).

38. CONCLUSIONS