scholarly journals Multisite functional connectivity MRI classification of autism: ABIDE results

2013 ◽  
Vol 7 ◽  
Author(s):  
Jared A. Nielsen ◽  
Brandon A. Zielinski ◽  
P. Thomas Fletcher ◽  
Andrew L. Alexander ◽  
Nicholas Lange ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Functional Connectivity Mri

scholarly journals Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

EBioMedicine ◽  
2018 ◽  
Vol 30 ◽  
pp. 74-85 ◽  
Author(s):  
Ling-Li Zeng ◽  
Huaning Wang ◽  
Panpan Hu ◽  
Bo Yang ◽  
Weidan Pu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Functional Connectivity ◽  
Diagnostic Classification ◽  
Functional Connectivity Mri

scholarly journals Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

2016 ◽  
Vol 19 (4) ◽  
pp. 581-598 ◽  
Author(s):  
Deanna J. Greene ◽  
Jessica A. Church ◽  
Nico U.F. Dosenbach ◽  
Ashley N. Nielsen ◽  
Babatunde Adeyemo ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Tourette Syndrome ◽  
Pattern Classification ◽  
Multivariate Pattern ◽  
Functional Connectivity Mri

scholarly journals Unsupervised classification of major depression using functional connectivity MRI

2013 ◽  
Vol 35 (4) ◽  
pp. 1630-1641 ◽  
Author(s):  
Ling-Li Zeng ◽  
Hui Shen ◽  
Li Liu ◽  
Dewen Hu
Keyword(s):  
Major Depression ◽  
Functional Connectivity ◽  
Unsupervised Classification ◽  
Functional Connectivity Mri

scholarly journals Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Evolutionary Algorithms ◽  
Resting State ◽  
Optimization Methods ◽  
Resting State Fmri ◽  
Optimization Method ◽  
Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

scholarly journals Effects of Field-Map Distortion Correction on Resting State Functional Connectivity MRI

2017 ◽  
Vol 11 ◽  
Author(s):  
Hiroki Togo ◽  
Jaroslav Rokicki ◽  
Kenji Yoshinaga ◽  
Tatsuhiro Hisatsune ◽  
Hiroshi Matsuda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Distortion Correction ◽  
Resting State Functional Connectivity ◽  
Functional Connectivity Mri

scholarly journals In vivo visuotopic brain mapping with manganese-enhanced MRI and resting-state functional connectivity MRI

NeuroImage ◽  
2014 ◽  
Vol 90 ◽  
pp. 235-245 ◽  
Author(s):  
Kevin C. Chan ◽  
Shu-Juan Fan ◽  
Russell W. Chan ◽  
Joe S. Cheng ◽  
Iris Y. Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Mapping ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Enhanced Mri ◽  
Functional Connectivity Mri ◽  
Manganese Enhanced Mri
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