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Article Abstract
Statistical Parametric Mapping is a widely used package of software for brain image analysis. It has also been the vehicle for sustained theoretical innovation and global impact in cognitive neuroscience. What can we learn from its success as it reaches middle age?
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This 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.
View Article and Find Full Text PDFFrom mapping to explaining brain function: the generative theoretical ambition of SPM.
Cereb Cortex
August 2025
Statistical Parametric Mapping is a widely used package of software for brain image analysis. It has also been the vehicle for sustained theoretical innovation and global impact in cognitive neuroscience. What can we learn from its success as it reaches middle age?
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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.
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Med Eng Phys
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Biomedical Device Technology, Istanbul Aydın University, Istanbul, 34093, Istanbul, Turkey. Electronic address:
Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection.
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Concurrent recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) signals reveals cross-scale neurovascular dynamics crucial for explaining fundamental linkages between function and behaviors. However, MRI scanners generate artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background.
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