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Functional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal could reflect the activity of inhibitory interneurons. However, these data paint a complex picture, with numerous regulatory interactions, and with responses that sometimes seem to point in different directions. It is therefore not trivial how to quantify the relative contributions of the different cell types into a consensus view compatible with the considered data. To address this, we present a new model-driven meta-analysis, which provides a unified and quantitative explanation for the considered data. This model-driven analysis allows for quantification of the relative contribution of different cell types: the contribution to the BOLD-signal from the excitatory cells is <20 % and 50-80 % comes from the interneurons. Our analysis also provides a mechanistic explanation for the observed experiment-to-experiment differences. For instance, one of the reasons that data seem to point in different directions is a biphasic vascular response, with a transient increase and a subsequent decrease. Our model-based data analysis explains why this biphasic response appears only for high-intensity stimulations and not for low-intensity stimulations. In other words, our meta-analysis goes beyond a simple vote-by-majority and provides a single unified explanation for the considered data. This explanation provides a consensus view that constitutes a paradigm shift in how fMRI can, and cannot, be used to interpret neuronal activity.
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http://dx.doi.org/10.1016/j.compbiomed.2025.111014 | DOI Listing |
Comput Biol Med
September 2025
Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden; School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine
Functional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal could reflect the activity of inhibitory interneurons.
View Article and Find Full Text PDFSci Data
March 2025
RIoT Research Center, Independent University, Dhaka, 1229, Bangladesh.
Assessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting.
View Article and Find Full Text PDFbioRxiv
October 2024
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Neuroimage
April 2019
Department of Radiology and Biomedical Imaging, Yale University, 300 Cedar Street, New Haven, CT, 06519, USA; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland; Department of Psychiatry, Psychotherapy and Psy
Positive emotions facilitate cognitive performance, and their absence is associated with burdening psychiatric disorders. However, the brain networks regulating positive emotions are not well understood, especially with regard to engaging oneself in positive-social situations. Here we report convergent evidence from a multimodal approach that includes functional magnetic resonance imaging (fMRI) brain activations, meta-analytic functional characterization, Bayesian model-driven analysis of effective brain connectivity, and personality questionnaires to identify the brain networks mediating the cognitive up-regulation of positive-social emotions.
View Article and Find Full Text PDFPatient Educ Couns
October 2016
The University of Texas at Austin, School of Nursing, Austin, TX, USA.
Objectives: To conduct a model-driven meta-analysis of correlational research on psychological and motivational predictors of diabetes outcomes, with adherence factors as mediators.
Methods: A comprehensive literature search of published and unpublished studies located a sample of 775 individual correlational or predictive studies reported across 739 research reports.
Results: Results varied according to the outcome variable included in the regression models.