Mechanistic versus descriptive interpretation of brain activity pattern reconstruction.
- 1Institute of Neurosciences and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany.
- 2Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Abstract
The geometric eigenmodes, derived from the shape of the cortical surface, have been suggested as a better model for explaining brain function due to their high accuracy in reconstructing patterns of both spontaneous and stimulus-evoked brain activity patterns captured by functional MRI, when contrasted with alternative models including structural connectome derived bases. While a descriptive representation offers valuable utility for neuroimaging studies, it is necessary to examine the extent to which it supports a mechanistic explanation for human brain function. Although the observation that geometric eigenmodes accurately fit brain activity is intriguing, we argue that accepting them as a model of brain function risks conflating descriptive and mechanistic interpretations. While a representation that effectively describes the underlying geometry is inherently adept at fitting patterns within that geometric space; it does not necessarily shed light on mechanisms that give rise to the brain's functional attributes. To this end, we provide two lines of empirical results: a) geometric eigenmodes demonstrate a high flexibility when fit to a range of manipulated patterns, and b) localized parcel-based representations can reconstruct activity patterns as well. Based on those results, theoretical considerations, and previous data, we argue that more consideration is needed regarding the complementary roles of descriptive representation and mechanistic explanation in neuroimaging research. While we recognize the potential role of the brain's geometry in influencing its dynamics, assertions regarding its efficacy should be weighed against its inherent lack of specificity, biological interpretability, and anatomical evidence.
Related
- Maternal-Fetal Exposome and Early-Life Microbiota-Pattern Recognition Receptors Signaling in Inflammatory Bowel Disease Susceptibility: A Mechanistic Review.
- A mechanistic interpretation of mutation risk across biological scales
- SHAPPEing agent-based models to spatial data: Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints
- Box–Behnken design optimization of Allium cepa (onion) peels extract-loaded chitosan nanoparticles for enhanced antibacterial and antioxidant activity with in-silico mechanistic insights
- CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling
BibTeX
@article{jung2026mechanistic,
title = {{Mechanistic versus descriptive interpretation of brain activity pattern reconstruction.}},
author = {Kyesam Jung and Simon B Eickhoff and Kaustubh R Patil},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = oct,
doi = {10.1073/pnas.2609630123},
url = {https://europepmc.org/article/MED/42879487},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Mechanistic versus descriptive interpretation of brain activity pattern reconstruction.
AU - Kyesam Jung
AU - Simon B Eickhoff
AU - Kaustubh R Patil
PY - 2026
DA - 2026/10/15
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2609630123
UR - https://europepmc.org/article/MED/42879487
AB - The geometric eigenmodes, derived from the shape of the cortical surface, have been suggested as a better model for explaining brain function due to their high accuracy in reconstructing patterns of both spontaneous and stimulus-evoked brain activity patterns captured by functional MRI, when contrasted with alternative models including structural connectome derived bases. While a descriptive representation offers valuable utility for neuroimaging studies, it is necessary to examine the extent to which it supports a mechanistic explanation for human brain function. Although the observation that geometric eigenmodes accurately fit brain activity is intriguing, we argue that accepting them as a model of brain function risks conflating descriptive and mechanistic interpretations. While a representation that effectively describes the underlying geometry is inherently adept at fitting patterns within that geometric space; it does not necessarily shed light on mechanisms that give rise to the brain's functional attributes. To this end, we provide two lines of empirical results: a) geometric eigenmodes demonstrate a high flexibility when fit to a range of manipulated patterns, and b) localized parcel-based representations can reconstruct activity patterns as well. Based on those results, theoretical considerations, and previous data, we argue that more consideration is needed regarding the complementary roles of descriptive representation and mechanistic explanation in neuroimaging research. While we recognize the potential role of the brain's geometry in influencing its dynamics, assertions regarding its efficacy should be weighed against its inherent lack of specificity, biological interpretability, and anatomical evidence.
SN - 0027-8424
SN - 1091-6490
ER -