⚙
AI-generated deep summary
by claude@2026-07, 2026-07-15
· read from full text
ⓘ
The paper develops propagation mapping, an approach that models task-evoked brain activity as propagation of regional signal amplitudes along whole-brain topological routes, extending activity-flow mapping beyond univariate regional measures. Using functional connectomes and structural covariance networks from a normative sample of 1,000 participants, it reports that propagation patterns can be recovered with high accuracy (average R2 = 0.947, MAE = 0.155, RMSE = 0.229) and remain stable across different task contrasts, parcellation atlases, and variations in signal intensity and inter-regional distance; similar performance is also shown in an independent sample at the subject and group levels using resting-state low-frequency oscillation amplitude (n=189). A key caveat is that the method relies on normative connectomes that could homogenize subject-specific variance, although it redistributed individual variance along propagation routes (Cohen’s d = 0.10, p=0.17). The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Human brain mapping has traditionally relied on univariate approaches to characterize regional activity, whereas more recent work focuses on interactions between regions to capture network-level organization. Despite their parallel development, growing evidence suggests that integrating both approaches is critical for a comprehensive understanding of task-evoked brain activity. The present study introduces propagation mapping, an extension of activity flow mapping that model’s task-evoked brain activity as the propagation of regional signal amplitudes along whole-brain topological routes. This study aims to evaluate propagation maps as reliable neurobiological features for neuroimaging research. Using functional connectomes and structural covariance network derived from a large normative sample (n=1,000), propagation patterns of task-evoked activity were accurately captured (average R 2 = 0.947, MAE=0.155, and RMSE=0.229) across 94 participants. Mapping performance remained stable across different task contrasts, parcellation atlases, and signal intensity and spatial distance between regions. Similar performance was observed at both the subject and group levels in an independent sample using the amplitude of low-frequency oscillations during resting-state (n=189). Importantly, despite its reliance on normative connectomes which could homogenize subject-specific variance, propagation mapping instead redistributed individual variance along propagation routes (Cohen’s d = 0.10, p=0.17). As a biologically comprehensive representation of brain organization, propagation mapping offers a powerful and user-friendly alternative to traditional regional analyses and provides new avenues for discovery in neurological and psychiatric neuroimaging research.
Full text
2,280 characters
· extracted from
oa-doi-fallback
· click to expand
Abstract
Human brain mapping has traditionally relied on univariate approaches to characterize regional activity, whereas more recent work focuses on interactions between regions to capture network-level organization. Despite their parallel development, growing evidence suggests that integrating both approaches is critical for a comprehensive understanding of task-evoked brain activity. The present study introduces propagation mapping, an extension of activity flow mapping that model’s task-evoked brain activity as the propagation of regional signal amplitudes along whole-brain topological routes. This study aims to evaluate propagation maps as reliable neurobiological features for neuroimaging research. Using functional connectomes and structural covariance network derived from a large normative sample (n=1,000), propagation patterns of task-evoked activity were accurately captured (average R2 = 0.947, MAE=0.155, and RMSE=0.229) across 94 participants. Mapping performance remained stable across different task contrasts, parcellation atlases, and signal intensity and spatial distance between regions. Similar performance was observed at both the subject and group levels in an independent sample using the amplitude of low-frequency oscillations during resting-state (n=189). Importantly, despite its reliance on normative connectomes which could homogenize subject-specific variance, propagation mapping instead redistributed individual variance along propagation routes (Cohen’s d = 0.10, p=0.17). As a biologically comprehensive representation of brain organization, propagation mapping offers a powerful and user-friendly alternative to traditional regional analyses and provides new avenues for discovery in neurological and psychiatric neuroimaging research.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The manuscript has been revised which include the addition of 5 midbrain communities for each of the atlases, the addition of Schaefer-400 17 Network Atlas, the implementation of null distributions that account for spatial autocorrelation, the additional testing for signal intensity, the addition of R2, Mean Absolute Error, Root Mean Square Error for mapping accuracy. Moreover, the Toolbox has been updated.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.