Sala Llonch, RoserTudela Fernández, RaúlRoqué Greoles, Carles2025-09-182025-09-182025-06https://hdl.handle.net/2445/223250Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutors: Roser Sala-Llonch, Raúl TudelaBrain Functional Magnetic Resonance Image (fMRI) is a widely used non-invasive technique for measuring brain activity and mapping functional regions, but has a complex spatiotemporal structure which complicates the analysis. We present an extension to an existing qualitycontrol (QC) pipeline for resting-state fMRI that automatically detects previously underexplored periodic spatial artefacts. By applying a 3D Fourier transform across each volume and computing inter-slice Pearson correlations over time, we generate summary plots that highlight high-frequency peaks and abnormally elevated correlations, indicative of periodic noise. We integrate these diagnostics in the existing visual and quantitative QC report, allowing reviewers to assign a second periodic-noise PASS/MAYBE/NO-PASS decision based on the presence of periodic noise. In a cohort of 1,178 older adults from the A4 study, our method flagged 42.5% of scans for periodic artifacts that had passed conventional QC. In a classification analysis using a support vector machine with features extracted from the Fourier transform and the spatial correlation analyses against the expert QC labels, we obtained an overall accuracy of 79.5% with a recall for PASS of 92.0%.6 p.application/pdfengcc-by-nc-nd (c) Roqué, 2025http://creativecommons.org/licenses/by-nc-nd/3.0/es/Ressonància magnètica funcionalTransformacions de FourierTreballs de fi de grauFunctional magnetic resonance imagingFourier transformationsBachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess