Cognitive Impairment

Il Semeion collabora con vari Centri Medici per  la comprensione e la diagnosi precoce del decadimento cognitivo (come l’ Alzheimer e il  Mild Cognitive Impairment (MCI)).

Numerose le collaborazioni e gli articoli scientifici prodotti.

P.M. Buscema, F.Vernieri, G. Massini, F. Scrascia, M. Breda, P.M. Rossini, E. Grossi
An improved I-FAST system for the diagnosis of Alzheimer’s disease from unprocessed electroencephalograms by using robust invariant features
in Artificial Intelligence in Medicine 64 (2015) 59–74. Elsevier, May 2015

M. Gironi, B. Borgiani, E. Farina, E. Mariani, C. Cursano, M. Alberoni, R. Nemni, G. Comi, P.M. Buscema, R. Furlan, E. Grossi
A Global Immune Deficit in Alzheimer’s Disease and Mild Cognitive Impairment Disclosed by a Novel Data Mining Process
in  Journal of Alzheimer’s Disease 43 (2015) 1199–1213. DOI 10.3233/JAD-141116.

C. Laske, H.R. Sohrabi, S.M. Frost, K. Lopez-de-Ipina, P. Garrard, P.M. Buscema, J. Dauwels, S.R. Soekadar, S. Mueller, C. Linnemann, S.A. Bridenbaugh, Y. Kanagasingam, R.N. Martins, S.E. O’Bryant
Innovative diagnostic tools for early detection of Alzheimer’s disease
in Alzheimer’s & Dementia, Eselvier Editore, dicember 2014.

F. Coppedè, E. Grossi, P.M. Buscema, L. Migliore
Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals
in Plos One, 2013, Vol 8 Issue 8

T. Gomiero, L. Croce, E. Grossi, L. De Vreese, P.M. Buscema, U.Mantesso, E. De Bastiani
A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems
in US-China Education Review, ISSN 1548-6613

F. Licastro, E.Porcellini, P. Forti. R. Borghi, P.M. Buscema, I. Carbone, P. Bossu, G. Ravaglia, E. Grossi
Multifactorial interactions in the pathogenesis pathway of Alzheimer’s disease: a new risk charts for prevention of dementia
in Immunity & Ageing, 2010, 7(Suppl1): S4

M. Tabaton, P.Odetti, S. Cammarata. R. Borghi, F. Monacelli, C. Caltagirone, P. Bossu, P.M. Buscema, E. Grossi
Artificial Neural Network identify the predictive values of risk fact of the amnestic mild cognitive impairment
in Journal of Alzheimer Disease, 19-2010, 1035-1040

P.M. Buscema, E. Grossi, M. Capriotti, C. Babiloni, P.M. Rossini
The I.F.A.S.T. Model Allows the Prediction of Conversion to Alzheimer Disease in Patients with Mild Cognitive Impairment with High Degree of Accuracy
in Current Alzheimer Research, 2010, 7, 173-187

P.M. Rossini, P.M. Buscema, E. Grossi
The Transferal of Evidences Derived from Clinical Research to Single Patient Level: Automatic Distinction of Normal Elderly vs Mild Cognitive Impairment Subjects by Resting EEG Data Processed by IFAST, a Novel Intelligent System
in Proceedings NAFIPS 2008

P.M. Buscema, E. Grossi, D. Snowdon, P. Antuono
Auto-Contractive Maps: an artificial adaptive system for data mining. An application to Alzheimer Disease
in Current Alzheimer Research, 2008 , 5, 481-498

P.M. Rossini, P.M. Buscema, M. Capriotti, E. Grossi, G. Rodriguez, C. Del Percio, C. Babiloni
Is it possible to automaticaly distinguish resting EEG data of normal elderly vs mild cognitive impairment subjects with high degree of accuracy?
in Clinical Neurophysiology, 119 (2008) pp 1534-1545

P.M Buscema, M. Capriotti, F. Bergami, C. Babiloni, P. Rossini, E.Grossi
The Implicit Function as Squashing Time Model: a novel parallel nonlinear EEG analysis technique distinguishing mild cognitive impairment and Alzheimer’s Disease subjects with high degree of accuracy
in Computational Intelligence and Neuroscience, Volume 2007

P.M. Buscema, P. Rossini, C. Babiloni, E. Grossi
The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer’s disease patients with high degree of accuracy
in Artificial Intelligence in Medicine 39 (2007) 40, 127-141

L.P. De Vreese, S. Pradelli, G. Massini, P.M. Buscema, R. Savarè, E. Grossi
The Travelling Salesman Problem as a new screening test in early Alzheimer’s disease: an explorarory study. Visual problem solving in AD
in Aging Clinical and Experimental Research , 2005, pp. 458-464

M. Di Luca, E. Grossi, B. Borroni, M. Zimmerman, E. Marcello, F. Colciaghi, F. Gardoni, M. Intraligi, A. Padovani e P.M. Buscema
Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease
in Journal of Translational Medicine, 2005, 3:30

E. Grossi e P.M. Buscema
The potential role played by artificial adaptive systems enhancing our understanding of Alzheimer Disease. The italian experience within Italian Interdisciplinary Network on Alzheimer Disease
in Neuroscience research communication , Vol. 35, No 3

E. Grossi, G. Massini, P.M. Buscema, R. Savarè, G. Maurelli
Two Different Alzheimer Diseases in Men and Women: Clues From Advanced Neural Networks and Artificial Intelligence
in Gender Medicine, 2005, Vol. 2, N. 2

P.M. Buscema, E. Grossi, D. Snowdon, P. Antuono, M. Intraligi, G. Maurelli, R. Savarè
Artificial Neural Network can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status
in Neuroinformatics, Winter 2004 2(4), pp. 399-416, Humana Press

P. Mecocci, E. Grossi, P.M. Buscema, M. Intraligi, V. Serafini, R. Savarè, P. Rinaldi, A. Cherubini, U. Senin
Use of Artificial Networks in clinical trials: a pilot study to predict responsiveness to donepezil in alzheimer’s Disease
in JAGS (Journal of American Geriatric Society), vol. 50, n. 11, November 2002, pp. 1857-1860

P. Mecocci, E. Grossi, P.M. Buscema, M. Intraligi, V. Serafini, R. Savarè, P. Rinaldi, A. Cherubini, U. Senin
Utilizzo delle reti neurali artificiali nei trial clinici: studio pilota per valutare la responsività al donepezil in pazienti con malattia di Alzheimer utilizzando un sistema di reti neurali
in Neurological Science, 2001, Springer–Verlag Ed., pp. 49-53.