PMID- 37555177
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230810
IS  - 2813-1193 (Electronic)
IS  - 2813-1193 (Linking)
VI  - 1
DP  - 2022
TI  - Identifying the Neural Correlates of Resting State Affect Processing Dynamics.
PG  - 825105
LID - 10.3389/fnimg.2022.825105 [doi]
LID - 825105
AB  - There exists growing interest in understanding the dynamics of resting state 
      functional magnetic resonance imaging (rs-fMRI) to establish mechanistic links 
      between individual patterns of spontaneous neural activation and corresponding 
      behavioral measures in both normative and clinical populations. Here we propose 
      and validate a novel approach in which whole-brain rs-fMRI data are mapped to a 
      specific low-dimensional representation-affective valence and arousal 
      processing-prior to dynamic analysis. This mapping process constrains the state 
      space such that both independent validation and visualization of the system's 
      dynamics become tractable. To test this approach, we constructed neural decoding 
      models of affective valence and arousal processing from brain states induced by 
      International Affective Picture Set image stimuli during task-related fMRI in (n 
      = 97) healthy control subjects. We applied these models to decode 
      moment-to-moment affect processing in out-of-sample subjects' rs-fMRI data and 
      computed first and second temporal derivatives of the resultant valence and 
      arousal time-series. Finally, we fit a second set of neural decoding models to 
      these derivatives, which function as neurally constrained ordinary differential 
      equations (ODE) underlying affect processing dynamics. To validate these 
      decodings, we simulated affect processing by numerical integration of the true 
      temporal sequence of neurally decoded derivatives for each subject and 
      demonstrated that these decodings generate significantly less (p < 0.05) 
      group-level simulation error than integration based upon decoded derivatives 
      sampled uniformly randomly from the true temporal sequence. Indeed, simulations 
      of valence and arousal processing were significant for up to four steps of 
      closed-loop simulation (Deltat = 2.0 s) for both valence and arousal, respectively. 
      Moreover, neural encoding representations of the ODE decodings include 
      significant clusters of activation within brain regions associated with affective 
      reactivity and regulation. Our work has methodological implications for efforts 
      to identify unique and actionable biomarkers of possible future or current 
      psychopathology, particularly those related to mood and emotional instability.
CI  - Copyright (c) 2022 Fialkowski and Bush.
FAU - Fialkowski, Kevin P
AU  - Fialkowski KP
AD  - Brain Imaging Research Center, University of Arkansas for Medical Sciences, 
      Little Rock, AR, United States.
FAU - Bush, Keith A
AU  - Bush KA
AD  - Brain Imaging Research Center, University of Arkansas for Medical Sciences, 
      Little Rock, AR, United States.
LA  - eng
PT  - Journal Article
DEP - 20220421
PL  - Switzerland
TA  - Front Neuroimaging
JT  - Frontiers in neuroimaging
JID - 9918402387106676
PMC - PMC10406310
OTO - NOTNLM
OT  - MVPA
OT  - affect
OT  - decoding
OT  - dynamics
OT  - emotion
OT  - fMRI
OT  - resting state
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- 2022/04/21 00:00
MHDA- 2022/04/21 00:01
PMCR- 2022/04/21
CRDT- 2023/08/09 04:11
PHST- 2021/11/30 00:00 [received]
PHST- 2022/03/07 00:00 [accepted]
PHST- 2022/04/21 00:01 [medline]
PHST- 2022/04/21 00:00 [pubmed]
PHST- 2023/08/09 04:11 [entrez]
PHST- 2022/04/21 00:00 [pmc-release]
AID - 10.3389/fnimg.2022.825105 [doi]
PST - epublish
SO  - Front Neuroimaging. 2022 Apr 21;1:825105. doi: 10.3389/fnimg.2022.825105. 
      eCollection 2022.