scholarly journals Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework

2012 ◽  
pp. 180-188 ◽  
Author(s):  
L. Chaari ◽  
F. Forbes ◽  
T. Vincent ◽  
P. Ciuciu
Keyword(s):  
Fmri Data ◽  
Joint Detection

scholarly journals Bayesian Joint Detection-Estimation of Cerebral Vasoreactivity from ASL fMRI Data

2013 ◽  
pp. 616-624 ◽  
Author(s):  
Thomas Vincent ◽  
Jan Warnking ◽  
Marjorie Villien ◽  
Alexandre Krainik ◽  
Philippe Ciuciu ◽  
...  
Keyword(s):  
Fmri Data ◽  
Joint Detection ◽  
Cerebral Vasoreactivity

Functional Anatomy of Intensity Aspects of Attention

2003 ◽  
Vol 14 (3) ◽  
pp. 181-190 ◽  
Author(s):  
Walter Sturm
Keyword(s):  
Right Hemisphere ◽  
Functional Anatomy ◽  
Fmri Data ◽  
Functional Networks ◽  
Top Down ◽  
Stimulus Modality ◽  
Spatial Orienting ◽  
Phasic Alertness ◽  
Co Activation ◽  
Attention System

Abstract: Behavioral and PET/fMRI-data are presented to delineate the functional networks subserving alertness, sustained attention, and vigilance as different aspects of attention intensity. The data suggest that a mostly right-hemisphere frontal, parietal, thalamic, and brainstem network plays an important role in the regulation of attention intensity, irrespective of stimulus modality. Under conditions of phasic alertness there is less right frontal activation reflecting a diminished need for top-down regulation with phasic extrinsic stimulation. Furthermore, a high overlap between the functional networks for alerting and spatial orienting of attention is demonstrated. These findings support the hypothesis of a co-activation of the posterior attention system involved in spatial orienting by the anterior alerting network. Possible implications of these findings for the therapy of neglect are proposed.

Explorative Analysis of fMRI Data: Why Functional PCA should Usually be Preferred to ICA

2004 ◽  
Vol 35 (03) ◽  
Author(s):  
R Viviani ◽  
G Groen ◽  
H Walter
Keyword(s):  
Fmri Data ◽  
Functional Pca

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

Classification of ASD based on fMRI data with deep learning

2021 ◽  
Author(s):  
Lizhen Shao ◽  
Cong Fu ◽  
Yang You ◽  
Dongmei Fu
Keyword(s):  
Deep Learning ◽  
Fmri Data

scholarly journals Adhd Classification From FMRI Data Using Fine Tunining in SVM

2021 ◽  
Vol 1937 (1) ◽  
pp. 012014
Author(s):  
S Anitha ◽  
S Thomas Geroge
Keyword(s):  
Fmri Data
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