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  Analysis strategies for high-resolution UHF-fMRI data

Polimeni, J., Renvall, V., Zaretskaya, N., & Fischl, B. (2018). Analysis strategies for high-resolution UHF-fMRI data. NeuroImage, 168, 296-320. doi:10.1016/j.neuroimage.2017.04.053.

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Polimeni, JR, Autor
Renvall, V, Autor
Zaretskaya, N1, 2, Autor           
Fischl, B, Autor
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Zusammenfassung: Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.

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 Datum: 2017-042018-03
 Publikationsstatus: Erschienen
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 Identifikatoren: DOI: 10.1016/j.neuroimage.2017.04.053
BibTex Citekey: PolimeniRZF2017
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Titel: NeuroImage
Genre der Quelle: Zeitschrift
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Seiten: - Band / Heft: 168 Artikelnummer: - Start- / Endseite: 296 - 320 Identifikator: -