title: The impact of physiological noise correction on fMRI at 7 T.
abstract: Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7 T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1 mm × 1.1 mm × 1.8 mm, 2 mm × 2 mm × 2 mm and 3 mm × 3 mm × 3 mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3mm isotropic resolution and whole brain image repetition time of 2s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR ~140. In conclusion, optimal physiological noise correction at 7 T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK. Electronic address: chutton@fil.ion.ucl.ac.uk.
authors.forename: C
authors.initials: C
authors.lastname: Hutton
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
authors.forename: O
authors.initials: O
authors.lastname: Josephs
authors.affiliation: Special Lab Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology, Magdeburg, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Stadler
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
authors.forename: E
authors.initials: E
authors.lastname: Featherstone
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
authors.forename: A
authors.initials: A
authors.lastname: Reid
authors.affiliation: Department of Biomedical Magnetic Resonance, Institute for Experimental Physics, Otto-von-Guericke University, Magdeburg, Germany.
authors.forename: O
authors.initials: O
authors.lastname: Speck
authors.affiliation: Institute for Biometry and Medical Informatics, Faculty of Medicine, Otto-von-Guericke University, Magdeburg, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Bernarding
authors.affiliation: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.
authors.forename: N
authors.initials: N
authors.lastname: Weiskopf
mesh_terms: D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging