Abstract #3277

Cluster Failure at (Ultra)high Resolution fMRI? Enhancing Accuracy in 7T Single-Subject Analysis Using Small Gaussian Kernels

Igor Fabian Tellez Ceja^1,2, Thomas Gladytz¹, Ludger Starke¹, Karsten Tabelow³, Thoralf Niendorf^4,5, and Henning Reimann¹

¹Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine, Berlin, Germany, ²Charité—Universitätsmedizin, Experimental and Clinical Research Center (ECRC), A Joint Cooperation between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine, Berlin, Germany, ³Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany, ⁴Max Delbrück Center for Molecular Medicine, Berlin, Germany, ⁵Charité—Universitätsmedizin Berlin, Experimental and Clinical Research Center (ECRC), A Joint Cooperation between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine, Berlin, Germany

Synopsis

Keywords: fMRI Analysis, fMRI (task based)

Motivation: Selecting the right Gaussian kernel size for fMRI lacks standardization. For the detection of subtle brain activations, smaller kernels are needed. However, their reliability in ensuring accurate and trustworthy results remains uncertain.

Goal(s): Evaluate the effectiveness of small Gaussian filter kernel on fMRI data.

Approach: Assessment of BOLD signal integrity, accuracy, and data normality- employing 7T fMRI simulated time series and resting-state data.

Results: The study underscored the efficiency of smaller kernels in minimizing noise and upholding accurate signal detection. Residuals largely followed a Gaussian distribution.

Impact: Our study provides factual support for using small Gaussian kernel sizes in 7T fMRI data for their reliability in both functionality and compliance with RFT requirements.

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