Is the brain macroscopically linear? A system identification of resting state dynamics Article Swipe
Erfan Nozari
,
Jennifer Stiso
,
Lorenzo Caciagli
,
Eli J. Cornblath
,
Xiaosong He
,
Maxwell A. Bertolero
,
Arun S. Mahadevan
,
George J. Pappas
,
Danielle S. Bassett
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.12.21.423856
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.12.21.423856
A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to exhibit an even larger repertoire of nonlinear behaviors. An implicit assumption has thus formed that an “accurate” computational model of whole-brain dynamics must inevitably be non-linear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide new evidence that challenges this assumption at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.
Related Topics
Concepts
Interpretability
Human Connectome Project
Computer science
Nonlinear system
Linear model
Computational model
Simplicity
Brain activity and meditation
Identification (biology)
Artificial intelligence
Residual
Resting state fMRI
Dynamics (music)
Machine learning
Statistical physics
Biological system
Algorithm
Neuroscience
Electroencephalography
Psychology
Functional connectivity
Physics
Quantum mechanics
Biology
Pedagogy
Botany
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.12.21.423856
- https://www.biorxiv.org/content/biorxiv/early/2021/08/11/2020.12.21.423856.full.pdf
- OA Status
- green
- Cited By
- 6
- References
- 83
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3113841452
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3113841452Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2020.12.21.423856Digital Object Identifier
- Title
-
Is the brain macroscopically linear? A system identification of resting state dynamicsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-22Full publication date if available
- Authors
-
Erfan Nozari, Jennifer Stiso, Lorenzo Caciagli, Eli J. Cornblath, Xiaosong He, Maxwell A. Bertolero, Arun S. Mahadevan, George J. Pappas, Danielle S. BassettList of authors in order
- Landing page
-
https://doi.org/10.1101/2020.12.21.423856Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/08/11/2020.12.21.423856.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/08/11/2020.12.21.423856.full.pdfDirect OA link when available
- Concepts
-
Interpretability, Human Connectome Project, Computer science, Nonlinear system, Linear model, Computational model, Simplicity, Brain activity and meditation, Identification (biology), Artificial intelligence, Residual, Resting state fMRI, Dynamics (music), Machine learning, Statistical physics, Biological system, Algorithm, Neuroscience, Electroencephalography, Psychology, Functional connectivity, Physics, Quantum mechanics, Biology, Pedagogy, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 1, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
83Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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