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Ashkan Faghiri
Georgia Institute of Technology
Sliding window protocolPsychologyNeurosciencePattern recognitionDynamic functional connectivity
4Publications
1H-index
15Citations
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Publications 6
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#1Ashkan Faghiri (UNM: University of New Mexico)H-Index: 1
#1Ashkan Faghiri (UNM: University of New Mexico)
Last. Adrian Preda (UNM: University of New Mexico)H-Index: 5
view all 14 authors...
Abstract Background Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC. New method Here we first calculate the gradients of each time series pair and use the magnitude of these...
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#1Ashkan Faghiri (UNM: University of New Mexico)
#2Julia M. Stephen (The Mind Research Network)H-Index: 22
Last. Vince D. Calhoun (UNM: University of New Mexico)H-Index: 93
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Studies of brain structure have shown that the cortex matures in both a linear and nonlinear manner depending on the time window and specific region studied. In addition, it has been shown that soc...
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#1Armin IrajiH-Index: 7
#2Ashkan FaghiriH-Index: 1
Last. Vince D. CalhounH-Index: 93
view all 9 authors...
1 CitationsSource
#1Ashkan FaghiriH-Index: 1
#2Armin IrajiH-Index: 7
Last. Vince D. CalhounH-Index: 93
view all 5 authors...
Studying functional network connectivity using different imaging modalities has been the focus of many studies in recent years. One category of methods assumes that the connectivity is constant throughout the whole scanning period (e.g. using Pearson correlation to estimate linear correlation between two time series each belonging to a specific region in brain) while others relax this assumption by estimating connectivity at different time scales. The most common way to estimate dynamic function...
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#1Flor A. Espinoza (Georgia Institute of Technology)H-Index: 6
#2Victor M. Vergara (Georgia Institute of Technology)H-Index: 1
Last. Vince D. CalhounH-Index: 93
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Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estima...
1 CitationsSource
#1Ashkan FaghiriH-Index: 1
#2Julia M. Stephen (The Mind Research Network)H-Index: 22
Last. Vince D. CalhounH-Index: 93
view all 5 authors...
Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting-state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3-22 from the PING study, we first performed group ICA to extract independent compo...
14 CitationsSource
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