PMID- 36086408
OWN - NLM
STAT- MEDLINE
DCOM- 20220913
LR  - 20221021
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2022
DP  - 2022 Jul
TI  - An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in 
      rs-fMRI Functional Network Connectivity.
PG  - 4449-4452
LID - 10.1109/EMBC48229.2022.9871548 [doi]
AB  - Dynamic functional network connectivity (dFNC) data extracted from resting state 
      functional magnetic resonance imaging (rs-fMRI) recordings has played a 
      significant role in characterizing the role that brain network interactions play 
      in a variety of brain disorders and cognitive functions. dFNC analyses frequently 
      use clustering methods to identify states of network activity. However, it is 
      possible that these states are dominated by a few highly influential networks or 
      nodes, which could obscure condition-related insights that might be gained from 
      networks or nodes less influential to the clustering. In this study, we propose a 
      novel feature learning-based approach that could contribute to the identification 
      of condition-related activity in formerly less influential networks or nodes. We 
      demonstrate the viability of our approach within the context of schizophrenia 
      (SZ), applying our approach to a dataset consisting of 151 participants with SZ 
      and 160 controls (HCs). We find that the removal of some connectivity pairs 
      significantly affects the underlying states and magnifies the differences between 
      participants with SZ and HCs in each state. Given our findings, we hope that our 
      approach will contribute to the characterization and improved diagnosis of a 
      variety of neurological conditions and functions. Clinical Relevance- Our 
      approach could contribute to the characterization and diagnosis of many 
      neurological conditions.
FAU - Ellis, Charles A
AU  - Ellis CA
FAU - Sendi, Mohammad S E
AU  - Sendi MSE
FAU - Miller, Robyn L
AU  - Miller RL
FAU - Calhoun, Vince D
AU  - Calhoun VD
LA  - eng
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Brain/diagnostic imaging
MH  - *Brain Mapping/methods
MH  - Humans
MH  - Learning
MH  - *Magnetic Resonance Imaging/methods
MH  - Nerve Net/diagnostic imaging
EDAT- 2022/09/11 06:00
MHDA- 2022/09/14 06:00
CRDT- 2022/09/10 01:06
PHST- 2022/09/10 01:06 [entrez]
PHST- 2022/09/11 06:00 [pubmed]
PHST- 2022/09/14 06:00 [medline]
AID - 10.1109/EMBC48229.2022.9871548 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4449-4452. doi: 
      10.1109/EMBC48229.2022.9871548.