PMID- 35516809
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220507
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 16
DP  - 2022
TI  - Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for 
      Constructing and Investigating Functional Brain Motifs.
PG  - 770468
LID - 10.3389/fnins.2022.770468 [doi]
LID - 770468
AB  - The study of brain network connectivity as a time-varying property began 
      relatively recently and, to date, has remained primarily concerned with capturing 
      a handful of discrete static states that characterize connectivity as measured on 
      a timescale shorter than that of the full scan. Capturing group-level 
      representations of temporally evolving patterns of connectivity is a challenging 
      and important next step in fully leveraging the information available in large 
      resting state functional magnetic resonance imaging (rs-fMRI) studies. We 
      introduce a flexible, extensible data-driven framework for the stable 
      identification of group-level multiframe (movie-style) dynamic functional network 
      connectivity (dFNC) states. Our approach employs uniform manifold approximation 
      and embedding (UMAP) to produce a continuity-preserving planar embedding of 
      high-dimensional time-varying measurements of whole-brain functional network 
      connectivity. Planar linear exemplars summarizing dominant dynamic trends across 
      the population are computed from local linear approximations to the 
      two-dimensional 2D embedded trajectories. A high-dimensional representation of 
      each 2D exemplar segment is obtained by averaging the dFNC observations 
      corresponding to the n planar nearest neighbors of tau evenly spaced points along 
      the 2D line segment representation (where n is the UMAP number-of-neighbors 
      parameter and tau is the temporal duration of trajectory segments being 
      approximated). Each of the 2D exemplars thus "lifts" to a multiframe 
      high-dimensional dFNC trajectory of length tau. The collection of high-dimensional 
      temporally evolving dFNC representations (EVOdFNCs) derived in this manner are 
      employed as dynamic basis objects with which to characterize observed 
      high-dimensional dFNC trajectories, which are then expressed as weighted 
      combination of these basis objects. Our approach yields new insights into 
      anomalous patterns of fluidly varying whole-brain connectivity that are 
      significantly associated with schizophrenia as a broad diagnosis as well as with 
      certain symptoms of this serious disorder. Importantly, we show that relative to 
      conventional hidden Markov modeling with single-frame unvarying dFNC summary 
      states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia 
      including hallucinations and delusions, suggesting that a more dynamic 
      characterization is needed to help illuminate such a complex brain disorder.
CI  - Copyright (c) 2022 Miller, Vergara, Pearlson and Calhoun.
FAU - Miller, Robyn L
AU  - Miller RL
AD  - The Tri-Institutional Center for Translational Research in Neuroimaging and Data 
      Science (TReNDS): Georgia State University, Georgia Institute of Technology and 
      Emory University, Atlanta, GA, United States.
FAU - Vergara, Victor M
AU  - Vergara VM
AD  - The Tri-Institutional Center for Translational Research in Neuroimaging and Data 
      Science (TReNDS): Georgia State University, Georgia Institute of Technology and 
      Emory University, Atlanta, GA, United States.
FAU - Pearlson, Godfrey D
AU  - Pearlson GD
AD  - Yale School of Medicine, New Haven, CT, United States.
FAU - Calhoun, Vince D
AU  - Calhoun VD
AD  - The Tri-Institutional Center for Translational Research in Neuroimaging and Data 
      Science (TReNDS): Georgia State University, Georgia Institute of Technology and 
      Emory University, Atlanta, GA, United States.
LA  - eng
PT  - Journal Article
DEP - 20220419
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC9063321
OTO - NOTNLM
OT  - dynamic functional network connectivity (dFNC)
OT  - functional magnetic resonance imaging (fMRI)
OT  - functional network connectivity (FNC)
OT  - resting state fMRI
OT  - schizophrenia
OT  - uniform manifold approximation and embedding (UMAP)
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/05/07 06:00
MHDA- 2022/05/07 06:01
PMCR- 2022/01/01
CRDT- 2022/05/06 05:45
PHST- 2021/09/03 00:00 [received]
PHST- 2022/01/24 00:00 [accepted]
PHST- 2022/05/06 05:45 [entrez]
PHST- 2022/05/07 06:00 [pubmed]
PHST- 2022/05/07 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2022.770468 [doi]
PST - epublish
SO  - Front Neurosci. 2022 Apr 19;16:770468. doi: 10.3389/fnins.2022.770468. 
      eCollection 2022.