PMID- 38293124
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240207
DP  - 2024 Jan 16
TI  - Dynamical models reveal anatomically reliable attractor landscapes embedded in 
      resting state brain networks.
LID - 2024.01.15.575745 [pii]
LID - 10.1101/2024.01.15.575745 [doi]
AB  - Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) 
      have generated many insights into cognition. However, the mechanistic 
      underpinnings of FC and RSNs are still not well-understood. It remains debated 
      whether resting state activity is best characterized as noise-driven fluctuations 
      around a single stable state, or instead, as a nonlinear dynamical system with 
      nontrivial attractors embedded in the RSNs. Here, we provide evidence for the 
      latter, by constructing whole-brain dynamical systems models from individual 
      resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized 
      NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural 
      masses representing brain parcels, connected by fully trainable, individualized 
      weights. We found that our models manifested a diverse taxonomy of nontrivial 
      attractor landscapes including multiple equilibria and limit cycles. However, 
      when projected into anatomical space, these attractors mapped onto a limited set 
      of canonical RSNs, including the default mode network (DMN) and frontoparietal 
      control network (FPN), which were reliable at the individual level. Further, by 
      creating convex combinations of models, bifurcations were induced that 
      recapitulated the full spectrum of dynamics found via fitting. These findings 
      suggest that the resting brain traverses a diverse set of dynamics, which 
      generates several distinct but anatomically overlapping attractor landscapes. 
      Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss 
      critical attractor properties and structure within the resting brain. Instead, 
      these may be better captured through neural dynamical modeling and analytic 
      approaches. The results provide new insights into the generative mechanisms and 
      intrinsic spatiotemporal organization of brain networks.
FAU - Chen, Ruiqi
AU  - Chen R
AUID- ORCID: 0000-0001-7770-9307
AD  - Division of Biology and Biomedical Sciences, Washington University in St. Louis, 
      St. Louis, MO 63108.
FAU - Singh, Matthew
AU  - Singh M
AUID- ORCID: 0000-0003-0051-336X
AD  - Department of Electrical and Systems Engineering, Washington University in St. 
      Louis, St. Louis, MO 63108.
FAU - Braver, Todd S
AU  - Braver TS
AUID- ORCID: 0000-0002-2631-3393
AD  - Department of Psychological & Brain Sciences, Washington University in St. Louis, 
      St. Louis, MO 63108.
FAU - Ching, ShiNung
AU  - Ching S
AUID- ORCID: 0000-0003-4063-7068
AD  - Department of Electrical and Systems Engineering, Washington University in St. 
      Louis, St. Louis, MO 63108.
LA  - eng
GR  - R01 NS130693/NS/NINDS NIH HHS/United States
GR  - R21 MH132240/MH/NIMH NIH HHS/United States
PT  - Preprint
DEP - 20240116
PL  - United States
TA  - bioRxiv
JT  - bioRxiv : the preprint server for biology
JID - 101680187
PMC - PMC10827065
OTO - NOTNLM
OT  - Bifurcations
OT  - Dynamical systems modeling
OT  - Individual differences
OT  - Resting state fMRI
OT  - Resting state networks
COIS- The authors declare no competing interests.
EDAT- 2024/01/31 06:43
MHDA- 2024/01/31 06:44
PMCR- 2024/01/30
CRDT- 2024/01/31 04:21
PHST- 2024/01/31 06:43 [pubmed]
PHST- 2024/01/31 06:44 [medline]
PHST- 2024/01/31 04:21 [entrez]
PHST- 2024/01/30 00:00 [pmc-release]
AID - 2024.01.15.575745 [pii]
AID - 10.1101/2024.01.15.575745 [doi]
PST - epublish
SO  - bioRxiv [Preprint]. 2024 Jan 16:2024.01.15.575745. doi: 
      10.1101/2024.01.15.575745.