Abstract
Research in recent years has provided some evidence of temporal non-stationarity of functional connectivity in resting state fMRI. In this paper, we present a novel methodology that can decode connectivity dynamics into a temporal sequence of hidden network "states" for each subject, using a Hidden Markov Modeling (HMM) framework. Each state is characterized by a unique covariance matrix or whole-brain network. Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs). Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices. Results on simulated fMRI data show that our method can effectively recover underlying basis networks as well as hidden states. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional activity of a subject at any point during the scan is composed of combinations of overlapping task-positive/negative pairs of networks as revealed by our basis. Distinct hidden temporal states are produced due to a different set of basis networks dominating the covariance pattern in each state.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 426-437 |
| Number of pages | 12 |
| Journal | Information processing in medical imaging : proceedings of the ... conference |
| Volume | 23 |
| State | Published - 2013 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Unsupervised learning of functional network dynamics in resting state fMRI. / Eavani, Harini; Satterthwaite, Theodore D.; Gur, Raquel E.; Gur, Ruben C.; Davatzikos, Christos.
In: Information processing in medical imaging : proceedings of the ... conference, Vol. 23, 2013, p. 426-437.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Unsupervised learning of functional network dynamics in resting state fMRI.
AU - Eavani, Harini
AU - Satterthwaite, Theodore D.
AU - Gur, Raquel E.
AU - Gur, Ruben C.
AU - Davatzikos, Christos
PY - 2013
Y1 - 2013
N2 - Research in recent years has provided some evidence of temporal non-stationarity of functional connectivity in resting state fMRI. In this paper, we present a novel methodology that can decode connectivity dynamics into a temporal sequence of hidden network "states" for each subject, using a Hidden Markov Modeling (HMM) framework. Each state is characterized by a unique covariance matrix or whole-brain network. Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs). Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices. Results on simulated fMRI data show that our method can effectively recover underlying basis networks as well as hidden states. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional activity of a subject at any point during the scan is composed of combinations of overlapping task-positive/negative pairs of networks as revealed by our basis. Distinct hidden temporal states are produced due to a different set of basis networks dominating the covariance pattern in each state.
AB - Research in recent years has provided some evidence of temporal non-stationarity of functional connectivity in resting state fMRI. In this paper, we present a novel methodology that can decode connectivity dynamics into a temporal sequence of hidden network "states" for each subject, using a Hidden Markov Modeling (HMM) framework. Each state is characterized by a unique covariance matrix or whole-brain network. Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs). Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices. Results on simulated fMRI data show that our method can effectively recover underlying basis networks as well as hidden states. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional activity of a subject at any point during the scan is composed of combinations of overlapping task-positive/negative pairs of networks as revealed by our basis. Distinct hidden temporal states are produced due to a different set of basis networks dominating the covariance pattern in each state.
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M3 - Article
VL - 23
SP - 426
EP - 437
JO - Information processing in medical imaging : proceedings of the ... conference
JF - Information processing in medical imaging : proceedings of the ... conference
SN - 1011-2499
ER -