Title: Peaks2Image: Reconstructing fMRI Statistical Maps from Peaks. Abstract: Neuroscience is striving to overcome the lack of power due to the small sample size of standard studies. An important step forward has been the creation of large-scale public image repositories, such as NeuroVault. Such repositories enable comparing images across studies and automatically associating them with cognitive terms. Yet, this type of meta-analysis faces a major roadblock: the scarcity and inconsistency of image annotations and metadata. Another resource containing rich annotations is the neuroscientific literature. However it only yields a handful of brain-space coordinates per publication, those of the main activity peaks reported in each study. In this work, we propose Peaks2Image, a neuralnetwork approach to reconstructing continuous spatial representations of brain activity from peak activation tables. Using reconstructions of studies published in the neuroscientific literature, we train a decoder using tf-idf features as labels, leading to a much broader set of decoded terms than current image-based studies. We validate the decoder on 43,000 NeuroVault images, successfully decoding 58 out of 81 concepts in a zero-shot setting.