Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory

Clinical Neurophysiology

Volumn 126, Issue 11, 2015, Pages 2132-2141

  Khazaee, Ali ^a   Ebrahimzadeh, Ata ^a   Babajani Feremi, Abbas ^b,c  

^a BABOL NOSHIRVANI UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^c LE BONHEUR CHILDREN S HOSPITAL   (United States)

Author keywords

Alzheimer's disease (AD); Graph theory; Machine learning; Resting state functional magnetic resonance imaging (rs fMRI); Statistical analysis

Indexed keywords

ADULT; AGED; ALGORITHM; ALZHEIMER DISEASE; ANATOMICAL VARIATION; ARTICLE; AUTOMATION; BRAIN REGION; CALCULATION; CLINICAL ARTICLE; CLINICAL ASSESSMENT; CLINICAL FEATURE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE SEVERITY; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; GRAPH THEORY; HUMAN; MACHINE LEARNING; MALE; MATHEMATICAL ANALYSIS; PATIENT IDENTIFICATION; PATTERN RECOGNITION; PRIORITY JOURNAL; RESTING STATE NETWORK; SUPPORT VECTOR MACHINE; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN MAPPING; CASE CONTROL STUDY; NERVE CELL NETWORK; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; REST; STATISTICAL ANALYSIS; THEORETICAL MODEL;

AGED; ALGORITHMS; ALZHEIMER DISEASE; BRAIN; BRAIN MAPPING; CASE-CONTROL STUDIES; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MODELS, THEORETICAL; NERVE NET; PATTERN RECOGNITION, AUTOMATED; REST; SUPPORT VECTOR MACHINE;

EID: 84952636562     PISSN: 13882457     EISSN: 18728952     Source Type: Journal    
DOI: 10.1016/j.clinph.2015.02.060     Document Type: Article

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