TY - GEN
T1 - Going from lines to triangles
T2 - 14th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2022
AU - Faghiri, Ashkan
AU - Iraji, Armin
AU - Lewis, Noah
AU - Yang, Kun
AU - Ishizuka, Koko
AU - Sawa, Akira
AU - Adali, Tulay
AU - Calhoun, Vince
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Statistical moments provide us with quantitative measures of the data and estimating these moments in a time-resolved fashion is challenging but important, especially for analyzing human brain data. In this work, we present our attempt at providing a general pipeline for finding different moments (of different orders), which are also resolved in both time and frequency. We showcase the application of our method by analyzing a resting-state functional magnetic resonance imaging dataset. Here we find that individuals with first-episode psychosis (FEP) stay significantly more (compared to controls) in a state which has a very strong pair-wise (two-way) connectivity between salience and default mode networks. That same state also shows strong tri-wise (three-way) connectivity with networks other than the default mode, which points to the fact that different interaction orders have different and complementary information. Our proposed approach can extract meaningful and structured information by calculating (typically ignored) different order moments that are resolved in both time and frequency of the moment space as opposed to sample space.
AB - Statistical moments provide us with quantitative measures of the data and estimating these moments in a time-resolved fashion is challenging but important, especially for analyzing human brain data. In this work, we present our attempt at providing a general pipeline for finding different moments (of different orders), which are also resolved in both time and frequency. We showcase the application of our method by analyzing a resting-state functional magnetic resonance imaging dataset. Here we find that individuals with first-episode psychosis (FEP) stay significantly more (compared to controls) in a state which has a very strong pair-wise (two-way) connectivity between salience and default mode networks. That same state also shows strong tri-wise (three-way) connectivity with networks other than the default mode, which points to the fact that different interaction orders have different and complementary information. Our proposed approach can extract meaningful and structured information by calculating (typically ignored) different order moments that are resolved in both time and frequency of the moment space as opposed to sample space.
KW - beyond pairwise
KW - fMRI
KW - functional connectivity
KW - statistical moments
KW - time-frequency analysis
UR - http://www.scopus.com/inward/record.url?scp=85135148615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135148615&partnerID=8YFLogxK
U2 - 10.1109/IVMSP54334.2022.9816296
DO - 10.1109/IVMSP54334.2022.9816296
M3 - Conference contribution
AN - SCOPUS:85135148615
T3 - IVMSP 2022 - 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop
BT - IVMSP 2022 - 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2022 through 29 June 2022
ER -