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This guide is primarily aimed at researchers from AI and machine learning backgrounds who may not be familiar with neuroimaging methodology. Reconstruction from neuroimaging data has recently gained popularity at major AI conferences, but many approaches fall into common traps that are well known within neuroscience. These pitfalls can lead to misleading results, often due to misunderstandings about the nature of fMRI data or the limitations of datasets originally collected for other research questions. For a detailed discussion of such issues in recent reconstruction pipelines, see: Shirakawa, K. et al. (2025). Spurious reconstruction from brain activity, Neural Networks .
Author Correction: Programmable 200 GOPS Hopfield-inspired photonic Ising machine