Information content of brain states is explained by structural constraints on state energetics Article Swipe
Leon Weninger
,
Pragya Srivastava
,
Dale Zhou
,
Jason Z. Kim
,
Eli J. Cornblath
,
Maxwell A. Bertolero
,
Ute Habel
,
Dorit Merhof
,
Dani S. Bassett
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1103/physreve.106.014401
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1103/physreve.106.014401
Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. Being the physical substrate upon which information propagates, the structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in functional magnetic resonance imaging (fMRI) data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on cognitive context; its absolute level and spatial distribution depend on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions-especially those to high information content states-are less costly than expected from random network null models, thereby indicating the brains marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physreve.106.014401
- OA Status
- green
- Cited By
- 14
- References
- 117
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W3208993767Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physreve.106.014401Digital Object Identifier
- Title
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Information content of brain states is explained by structural constraints on state energeticsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-05Full publication date if available
- Authors
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Leon Weninger, Pragya Srivastava, Dale Zhou, Jason Z. Kim, Eli J. Cornblath, Maxwell A. Bertolero, Ute Habel, Dorit Merhof, Dani S. BassettList of authors in order
- Landing page
-
https://doi.org/10.1103/physreve.106.014401Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2110.13781Direct OA link when available
- Concepts
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Energetics, Content (measure theory), State (computer science), Statistical physics, Computer science, Chemistry, Thermodynamics, Physics, Mathematics, Algorithm, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 8, 2022: 5Per-year citation counts (last 5 years)
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117Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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