Literature DB >> 28985932

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

John M Billings1, Maxwell Eder1, William C Flood1, Devendra Singh Dhami2, Sriraam Natarajan2, Christopher T Whitlow3.   

Abstract

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computer science; Function MR imaging; MR imaging; Machine learning; Resting state function MR imaging

Mesh:

Year:  2017        PMID: 28985932     DOI: 10.1016/j.nic.2017.06.010

Source DB:  PubMed          Journal:  Neuroimaging Clin N Am        ISSN: 1052-5149            Impact factor:   2.264


  2 in total

1.  Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective.

Authors:  Jie Wei; Tong Chen; Chuandong Li; Guangyuan Liu; Jiang Qiu; Dongtao Wei
Journal:  Front Hum Neurosci       Date:  2018-10-18       Impact factor: 3.169

2.  Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study.

Authors:  Animesh Kumar Paul; Anushree Bose; Sunil Vasu Kalmady; Venkataram Shivakumar; Vanteemar S Sreeraj; Rujuta Parlikar; Janardhanan C Narayanaswamy; Serdar M Dursun; Andrew J Greenshaw; Russell Greiner; Ganesan Venkatasubramanian
Journal:  Front Psychiatry       Date:  2022-08-05       Impact factor: 5.435

  2 in total

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