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Background: Parametric mapping sequences in cardiovascular magnetic resonance (CMR) allow for non-invasive myocardial tissue characterization. However quantitative myocardial mapping is still limited by the need for local reference values. Confounders, such as field strength, vendors and sequences, make intersite comparisons challenging. This exploratory study aims to assess whether multi-site studies that control confounding factors provide first insights whether parametric mapping values are within pre-defined tolerance ranges across scanners and sites.
Methods: A cohort of 20 healthy travelling volunteers was prospectively scanned at three sites with a 3 T scanner from the same vendor using the same scanning protocol and acquisition scheme. A Modified Look-Locker inversion recovery sequence (MOLLI) for T1 and a fast low-angle shot sequence (FLASH) for T2 were used. At one site a scan-rescan was performed to assess the intra-scanner reproducibility. All acquired T1- and T2-mappings were analyzed in a core laboratory using the same post-processing approach and software.
Results: After exclusion of one volunteer due to an accidentally diagnosed cardiac disease, T1- and T2-maps of 19 volunteers showed no significant differences between the 3 T sites (mean ± SD [95% confidence interval] for global T1 in ms: site I: 1207 ± 32 [1192-1222]; site II: 1207 ± 40 [1184-1225]; site III: 1219 ± 26 [1207-1232]; p = 0.067; for global T2 in ms: site I: 40 ± 2 [39-41]; site II: 40 ± 1 [39-41]; site III 39 ± 2 [39-41]; p = 0.543).
Conclusion: Parametric mapping results displayed initial hints at a sufficient similarity between sites when confounders, such as field strength, vendor diversity, acquisition schemes and post-processing analysis are harmonized. This finding needs to be confirmed in a powered clinical trial. Trial registration ISRCTN14627679 (retrospectively registered).
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http://dx.doi.org/10.1186/s12968-023-00954-9 | DOI Listing |
Cereb Cortex
August 2025
Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
Over three decades, statistical parametric mapping has transformed neuroimaging from descriptive mapping to causal inference, placing generative models at the core of causal explanations for brain function. It inspired to a large degree The Virtual Brain, which builds subject-specific digital twins from multimodal data, enabling brain simulations and exploration. Both frameworks converge at parameter estimation, where model and data meet, providing the mathematical manifestation of cause-effect in pathophysiology.
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August 2025
The Clinical Hospital of Chengdu Brain Sciences Institute, University of Electronic Sciences and Technology of China (UESTC), 2006 Xiyuan Avenue, West Hi Tech Zone, 611731, Chengdu, China.
This commentary reflects three decades of interaction between the Cuban neuroinformatics tradition and the statistical parametric mapping (SPM) framework. From the early development of neurometrics in Cuba to global initiatives like the Global Brain Consortium, our trajectory has paralleled and intersected with that of SPM. We highlight shared commitments to generative modeling, Bayesian inference, and population-level brain mapping, as shaped through collaborations, workshops, and joint theoretical work with Karl Friston and his group.
View Article and Find Full Text PDFThis Editorial shares with the neuroscience community the signs of progress in making Cerebral Cortex more attractive. Furthermore, the journal commemorates the Statistical Parametric Mapping (SPM), introduced by Karl Friston and his collaborators three decades ago. Over time, SPM has had a profound impact on the way of thinking in neuroscience.
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August 2025
Research Imaging Institute, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229, United States.
Statistical Parametric Mapping (SPM) adheres to rigorous methodological standards, including: spatial normalization, inter-subject averaging, voxel-wise contrasts, and coordinate reporting. This rigor ensures that a thematically diverse literature is amenable to meta-analysis. BrainMap is a community database (www.
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August 2025
Functional Imaging Laboratory (FIL), Department of Imaging Neuroscience, University College London, 12 Queen Square, London WC1N 3AR, United Kingdom.
This paper marks the 30th anniversary of the Statistical Parametric Mapping (SPM) software and the journal Cerebral Cortex: two modest milestones that mark the inception of cognitive neuroscience. We take this opportunity to reflect on SPM, a generation after its introduction. Each of the authors of this paper-who represent a small selection of the many contributors to SPM-were asked to consider lessons learned, what has gone well, and where there is room for improvement in future development.
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