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Triggiani AI, Bevilacqua V, Brunetti A, et al. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks. Front Neurosci. 2017;10:604doi: 10.3389/fnins.2016.00604.
Triggiani, A. I., Bevilacqua, V., Brunetti, A., Lizio, R., Tattoli, G., Cassano, F., Soricelli, A., Ferri, R., Nobili, F., Gesualdo, L., Barulli, M. R., Tortelli, R., Cardinali, V., Giannini, A., Spagnolo, P., Armenise, S., Stocchi, F., Buenza, G., Scianatico, G., Logroscino, G., Lacidogna, G., Orzi, F., Buttinelli, C., Giubilei, F., Del Percio, C., Frisoni, G. B., & Babiloni, C. (2016). Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks. Frontiers in neuroscience, 10604. https://doi.org/10.3389/fnins.2016.00604
Triggiani, Antonio I, et al. "Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks." Frontiers in neuroscience vol. 10 (2016): 604. doi: https://doi.org/10.3389/fnins.2016.00604
Triggiani AI, Bevilacqua V, Brunetti A, Lizio R, Tattoli G, Cassano F, Soricelli A, Ferri R, Nobili F, Gesualdo L, Barulli MR, Tortelli R, Cardinali V, Giannini A, Spagnolo P, Armenise S, Stocchi F, Buenza G, Scianatico G, Logroscino G, Lacidogna G, Orzi F, Buttinelli C, Giubilei F, Del Percio C, Frisoni GB, Babiloni C. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks. Front Neurosci. 2017 Jan 26;10:604. doi: 10.3389/fnins.2016.00604. eCollection 2016. PMID: 28184183; PMCID: PMC5266711.
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Front Neurosci. 2017 Jan 26;10:604. doi: 10.3389/fnins.2016.00604. eCollection 2016.

Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks.

Frontiers in neuroscience

Antonio I Triggiani, Vitoantonio Bevilacqua, Antonio Brunetti, Roberta Lizio, Giacomo Tattoli, Fabio Cassano, Andrea Soricelli, Raffaele Ferri, Flavio Nobili, Loreto Gesualdo, Maria R Barulli, Rosanna Tortelli, Valentina Cardinali, Antonio Giannini, Pantaleo Spagnolo, Silvia Armenise, Fabrizio Stocchi, Grazia Buenza, Gaetano Scianatico, Giancarlo Logroscino, Giordano Lacidogna, Francesco Orzi, Carla Buttinelli, Franco Giubilei, Claudio Del Percio, Giovanni B Frisoni, Claudio Babiloni

Affiliations

  1. Department of Clinical and Experimental Medicine, University of Foggia Foggia, Italy.
  2. Department of Electrical and Information Engineering, Polytechnic of Bari Bari, Italy.
  3. Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza"Rome, Italy; Department of Neuroscience, IRCCS San Raffaele PisanaRome, Italy.
  4. Department of Integrated Imaging, IRCCS Istituto di Ricerca Diagnostica e NucleareNapoli, Italy; Department of Motor Sciences and Healthiness, University of Naples ParthenopeNaples, Italy.
  5. Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging Enna, Italy.
  6. Clinical Neurology Unit, Department of Neuroscience, University of Genoa and IRCCS Azienda Ospedaliera Universitaria San Martino-IST Genoa, Italy.
  7. Dipartimento Emergenza e Trapianti d'Organi, University of Bari Bari, Italy.
  8. Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy.
  9. Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy.
  10. Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. PanicoLecce, Italy; Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro"Bari, Italy.
  11. Department of Imaging-Division of Radiology, Hospital "Di Venere" Bari, Italy.
  12. Division of Neuroradiology, "F. Ferrari" Hospital Lecce, Italy.
  13. Department of Neuroscience, IRCCS San Raffaele Pisana Rome, Italy.
  14. Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. PanicoLecce, Italy; Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro"Bari, Italy.
  15. Center for Neuropsychological Research, Institute of Neurology of the Policlinico Gemelli/Catholic University of Rome Italy.
  16. Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza" Rome, Italy.
  17. Department of Integrated Imaging, IRCCS Istituto di Ricerca Diagnostica e Nucleare Napoli, Italy.
  18. Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS Centro "S. Giovanni di Dio-F.B.F."Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of GenevaGeneva, Switzerland.

PMID: 28184183 PMCID: PMC5266711 DOI: 10.3389/fnins.2016.00604

Abstract

Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2-4 Hz), theta (4-8 Hz Hz), alpha1 (8-10.5 Hz), and alpha2 (10.5-13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.

Keywords: Alzheimer's disease (AD); artificial neural networks (ANNs); electroencephalography (EEG); exact low-resolution brain electromagnetic tomography (eLORETA); linear lagged connectivity

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