There is growing interest in the description of short-lived patterns in the spatiotemporal cortical activity monitored via neuroimaging. Most traditional analysis methods, designed to estimate relatively long-term brain dynamics, are not always appropriate to capture these patterns. Here we introduce a novel data-driven approach for detecting short-lived fMRI brain activity patterns. Exploiting Density Peak Clustering (Rodriguez and Laio [2014]), our approach reveals well localized clusters by identifying and grouping together voxels whose time-series are similar, irrespective of their brain location, even when very short time windows (similar to 10 volumes) are used. The method, which we call Coherence Density Peak Clustering (CDPC), is first tested on simulated data and compared with a standard unsupervised approach for fMRI analysis, independent component analysis (ICA). CDPC identifies activated voxels with essentially no false-positives and proves more reliable than ICA, which is troubled by a number of false positives comparable to that of true positives. The reliability of the method is demonstrated on real fMRI data from a simple motor task, containing brief iterations of the same movement. The clusters identified are found in regions expected to be involved in the task, and repeat synchronously with the paradigm. The methodology proposed is especially suitable for the study of short-time brain dynamics and single trial experiments, where the event or task of interest cannot be repeated for the same subject, as happens, for instance, in problem-solving, learning and decision-making. A GUI implementation of our method is available for download at https://github.com/micheleallegra/CDPC. (C) 2016 Wiley Periodicals, Inc.