Functional and effective brain connectivity for discrimination between Alzheimer's patients and healthy individuals: A study on resting state EEG rhythms.
OBJECTIVE
This exploratory study provided a proof of concept of a new procedure using multivariate electroencephalographic (EEG) topographic markers of cortical connectivity to discriminate normal elderly (Nold) and Alzheimer's disease (AD) individuals.


METHODS
The new procedure was tested on an existing database formed by resting state eyes-closed EEG data (19 exploring electrodes of 10-20 system referenced to linked-ear reference electrodes) recorded in 42 AD patients with dementia (age: 65.9years±8.5 standard deviation, SD) and 42 Nold non-consanguineous caregivers (age: 70.6years±8.5 SD). In this procedure, spectral EEG coherence estimated reciprocal functional connectivity while non-normalized directed transfer function (NDTF) estimated effective connectivity. Principal component analysis and computation of Mahalanobis distance integrated and combined these EEG topographic markers of cortical connectivity. The area under receiver operating curve (AUC) indexed the classification accuracy.


RESULTS
A good classification of Nold and AD individuals was obtained by combining the EEG markers derived from NDTF and coherence (AUC=86%, sensitivity=0.85, specificity=0.70).


CONCLUSIONS
These encouraging results motivate a cross-validation study of the new procedure in age- and education-matched Nold, stable and progressing mild cognitive impairment individuals, and de novo AD patients with dementia.


CONCLUSIONS
If cross-validated, the new procedure will provide cheap, broadly available, repeatable over time, and entirely non-invasive EEG topographic markers reflecting abnormal cortical connectivity in AD patients diagnosed by direct or indirect measurement of cerebral amyloid β and hyperphosphorylated tau peptides.
Faculty of Physics, Department of Biomedical Physics, University of Warsaw, Warsaw, Poland; Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland. Electronic address: kjbli@fuw.edu.pl.
Katarzyna J
KJ
Blinowska
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Poland; Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland.
Franciszek
F
Rakowski
Faculty of Physics, Department of Biomedical Physics, University of Warsaw, Warsaw, Poland.
Maciej
M
Kaminski
Inria Paris, Aramis Project-team, 75013 Paris, France; Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM) - Hôpital Pitié-Salpêtrière, 75013 Paris, France.
Fabrizio
F
De Vico Fallani
Department of Integrated Imaging, IRCCS SDN, Napoli, Italy.
Claudio
C
Del Percio
IRCCS San Raffaele Pisana, Rome, Italy; Department of Physiology and Pharmacology, University of Rome "La Sapienza", Rome, Italy.
Roberta
R
Lizio
IRCCS San Raffaele Pisana, Rome, Italy; Department of Physiology and Pharmacology, University of Rome "La Sapienza", Rome, Italy.
Claudio
C
Babiloni
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000000981:diagnostic imaging; D016022:Case-Control Studies; D063132:Connectome; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged
Alzheimer’s disease; Classification accuracy; Directed transfer function (DTF); EEG rhythms; Effective connectivity; Granger causality; Receiver operating characteristic (ROC)