Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction
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چکیده
منابع مشابه
Comparison of Local Information Indices Applied in Resting State Functional Brain Network Connectivity Prediction
Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is "distance penalization." But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of ...
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Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-sta...
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Introduction Synchronized low-frequency fluctuations in resting-state fMRI (1) have been used to investigate functional connectivity between brain areas. Recent studies demonstrate that this technique can be utilized in the study of Alzheimers disease (2), antidepressant effects (3), and default mode of brain networks (4). So far, most resting-state fMRI studies have been performed on humans...
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With the introduction of electroencephalography (EEG) in 1930, researchers began to explore spontaneous activity in the brain by recording the individual, independently of any task. Subsequently, evoked potential studies, where electrical potentials were recorded at the onset of a stimulus, marked a milestone in brain research. Utilizing such methods coupled with experimental psychology, resear...
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The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional co...
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ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2016
ISSN: 1662-453X
DOI: 10.3389/fnins.2016.00585