title: Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.
abstract: BACKGROUND
  We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI).
  
  
  METHODS
  Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features.
  
  
  RESULTS
  Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD.
  
  
  METHODS
  To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC.
  
  
  CONCLUSIONS
  Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.
authors.affiliation: Department of Electrical Engineering, Babol University of Technology, Babol, Iran.
authors.forename: Seyed Hani
authors.initials: SH
authors.lastname: Hojjati
authors.affiliation: Department of Electrical Engineering, Babol University of Technology, Babol, Iran.
authors.forename: Ata
authors.initials: A
authors.lastname: Ebrahimzadeh
authors.affiliation: Department of Electrical Engineering, University of Bojnord, Bojnord, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Khazaee
authors.affiliation: Department of Pediatrics, Division of Clinical Neurosciences, University of Tennessee Health Science Center, Memphis, TN, USA; Neuroscience Institute and Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, TN, USA; Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA. Electronic address: ababajan@uthsc.edu.
authors.forename: Abbas
authors.initials: A
authors.lastname: Babajani-Feremi
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D019540:Area Under Curve; D001921:Brain / Q000000981:diagnostic imaging / Q000503:physiopathology; D060825:Cognitive Dysfunction / Q000145:classification / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D018450:Disease Progression; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D011379:Prognosis; D012372:ROC Curve; D012146:Rest; D060388:Support Vector Machine
keywords: Alzheimer’s disease (AD); Graph theory; MCI converter; MCI non-converter; Machine learning approach; Mild cognitive impairment (MCI); Resting-state fMRI