Literature DB >> 18002733

A supervised method to assist the diagnosis of Alzheimer's disease based on functional magnetic resonance imaging.

Evanthia E Tripoliti1, Dimitrios I Fotiadis, Maria Argyropoulou.   

Abstract

In this work we present a supervised method to assist the diagnosis of Alzheimer's disease (AD) based on functional magnetic resonance images (fMRI). The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling the way in which the BOLD response depends on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests algorithm. The proposed method is evaluated using data from 41 subjects (14 young adults, 14 non demented older adults and 13 demented older adults.

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Year:  2007        PMID: 18002733     DOI: 10.1109/IEMBS.2007.4353067

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  6 in total

1.  Molecular neuropsychology: creation of test-specific blood biomarker algorithms.

Authors:  Sid E O'Bryant; Guanghua Xiao; Robert Barber; C Munro Cullum; Myron Weiner; James Hall; Melissa Edwards; Paula Grammas; Kirk Wilhelmsen; Rachelle Doody; Ramon Diaz-Arrastia
Journal:  Dement Geriatr Cogn Disord       Date:  2013-01-03       Impact factor: 2.959

2.  Combining graph and machine learning methods to analyze differences in functional connectivity across sex.

Authors:  R Casanova; C T Whitlow; B Wagner; M A Espeland; J A Maldjian
Journal:  Open Neuroimag J       Date:  2012-01-26

3.  Correlates of Near-Infrared Spectroscopy Brain-Computer Interface Accuracy in a Multi-Class Personalization Framework.

Authors:  Sabine Weyand; Tom Chau
Journal:  Front Hum Neurosci       Date:  2015-09-30       Impact factor: 3.169

4.  Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI.

Authors:  Davud Asemani; Hassan Morsheddost; Mahsa Alizadeh Shalchy
Journal:  Healthc Technol Lett       Date:  2017-06-26

Review 5.  How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database.

Authors:  Stavros I Dimitriadis; Dimitris Liparas
Journal:  Neural Regen Res       Date:  2018-06       Impact factor: 5.135

Review 6.  Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review.

Authors:  Alessia Sarica; Antonio Cerasa; Aldo Quattrone
Journal:  Front Aging Neurosci       Date:  2017-10-06       Impact factor: 5.750

  6 in total

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