Precision fMRI and cluster‐failure in the individual brain
Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel size...
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| Published in | Human brain mapping Vol. 45; no. 12; pp. e26813 - n/a |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
15.08.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1065-9471 1097-0193 1097-0193 |
| DOI | 10.1002/hbm.26813 |
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| Abstract | Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level‐dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single‐subject analysis. We introduce adaptive‐weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster‐corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole‐brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family‐wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.
Functional magnetic resonance imaging (fMRI) at ≥7 T achieves finer spatial resolution, eventually causing a decrement in temporal signal‐to‐noise ratio and thus blood oxygen level‐dependent (BOLD) sensitivity. Adaptive‐weight smoothing with optimized metrics (AWSOM) enhances BOLD effects detection with high spatial accuracy and preserves the integrity of BOLD signal magnitudes. AWSOM minimizes family‐wise error rates by effectively suppressing false positives in single‐subject fMRI analysis. |
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| AbstractList | Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level‐dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single‐subject analysis. We introduce adaptive‐weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster‐corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole‐brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family‐wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.
Functional magnetic resonance imaging (fMRI) at ≥7 T achieves finer spatial resolution, eventually causing a decrement in temporal signal‐to‐noise ratio and thus blood oxygen level‐dependent (BOLD) sensitivity. Adaptive‐weight smoothing with optimized metrics (AWSOM) enhances BOLD effects detection with high spatial accuracy and preserves the integrity of BOLD signal magnitudes. AWSOM minimizes family‐wise error rates by effectively suppressing false positives in single‐subject fMRI analysis. Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level‐dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single‐subject analysis. We introduce adaptive‐weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster‐corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole‐brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family‐wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download. Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download.Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal-to-noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level-dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single-subject analysis. We introduce adaptive-weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster-corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole-brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family-wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download. Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level‐dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single‐subject analysis. We introduce adaptive‐weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster‐corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole‐brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family‐wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download. Functional magnetic resonance imaging (fMRI) at ≥7 T achieves finer spatial resolution, eventually causing a decrement in temporal signal‐to‐noise ratio and thus blood oxygen level‐dependent (BOLD) sensitivity. Adaptive‐weight smoothing with optimized metrics (AWSOM) enhances BOLD effects detection with high spatial accuracy and preserves the integrity of BOLD signal magnitudes. AWSOM minimizes family‐wise error rates by effectively suppressing false positives in single‐subject fMRI analysis. Advances in neuroimaging acquisition protocols and denoising techniques, along with increasing magnetic field strengths, have dramatically improved the temporal signal‐to‐noise ratio (tSNR) in functional magnetic resonance imaging (fMRI). This permits spatial resolution with submillimeter voxel sizes and ultrahigh temporal resolution and opens a route toward performing precision fMRI in the brains of individuals. Yet ultrahigh spatial and temporal resolution comes at a cost: it reduces tSNR and, therefore, the sensitivity to the blood oxygen level‐dependent (BOLD) effect and other functional contrasts across the brain. Here we investigate the potential of various smoothing filters to improve BOLD sensitivity while preserving the spatial accuracy of activated clusters in single‐subject analysis. We introduce adaptive‐weight smoothing with optimized metrics (AWSOM), which addresses this challenge extremely well. AWSOM employs a local inference approach that is as sensitive as cluster‐corrected inference of data smoothed with large Gaussian kernels, but it preserves spatial details across multiple tSNR levels. This is essential for examining whole‐brain fMRI data because tSNR varies across the entire brain, depending on the distance of a brain region from the receiver coil, the type of setup, acquisition protocol, preprocessing, and resolution. We found that cluster correction in single subjects results in inflated family‐wise error and false positive rates. AWSOM effectively suppresses false positives while remaining sensitive even to small clusters of activated voxels. Furthermore, it preserves signal integrity, that is, the relative activation strength of significant voxels, making it a valuable asset for a wide range of fMRI applications. Here we demonstrate these features and make AWSOM freely available to the research community for download. |
| Author | Tabelow, Karsten Starke, Ludger Reimann, Henning Matthias Ceja, Igor Fabian Tellez Gladytz, Thomas Niendorf, Thoralf |
| AuthorAffiliation | 2 Charité—Universitätsmedizin Berlin Berlin Germany 3 Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany 1 Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.) Berlin Germany 4 Experimental and Clinical Research Center (ECRC), A Joint Cooperation between the Charité Medical Faculty and the Max‐Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany |
| AuthorAffiliation_xml | – name: 4 Experimental and Clinical Research Center (ECRC), A Joint Cooperation between the Charité Medical Faculty and the Max‐Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany – name: 2 Charité—Universitätsmedizin Berlin Berlin Germany – name: 3 Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany – name: 1 Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.) Berlin Germany |
| Author_xml | – sequence: 1 givenname: Igor Fabian Tellez orcidid: 0009-0008-0541-9536 surname: Ceja fullname: Ceja, Igor Fabian Tellez organization: Charité—Universitätsmedizin Berlin – sequence: 2 givenname: Thomas surname: Gladytz fullname: Gladytz, Thomas organization: Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.) – sequence: 3 givenname: Ludger surname: Starke fullname: Starke, Ludger organization: Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.) – sequence: 4 givenname: Karsten orcidid: 0000-0003-1274-9951 surname: Tabelow fullname: Tabelow, Karsten organization: Weierstrass Institute for Applied Analysis and Stochastics – sequence: 5 givenname: Thoralf surname: Niendorf fullname: Niendorf, Thoralf organization: Experimental and Clinical Research Center (ECRC), A Joint Cooperation between the Charité Medical Faculty and the Max‐Delbrück Center for Molecular Medicine in the Helmholtz Association – sequence: 6 givenname: Henning Matthias orcidid: 0000-0003-1053-9256 surname: Reimann fullname: Reimann, Henning Matthias email: henning.reimann@mdc-berlin.de organization: Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Ultrahigh Field Facility (B.U.F.F.) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39185695$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | adaptive weights smoothing Adult Blood levels BOLD Brain Brain - diagnostic imaging Brain - physiology Brain mapping Brain Mapping - methods Brain research Cluster Analysis cluster failure Cost analysis Error correction fMRI Functional magnetic resonance imaging Humans Image Processing, Computer-Assisted - methods Inference Magnetic fields Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Neuroimaging Neurosciences Oxygen - blood Sensitivity analysis Signal integrity Signal to noise ratio single‐subject Smoothing spatial accuracy Spatial discrimination Spatial resolution Spatial smoothing Statistical inference Temporal resolution |
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| Title | Precision fMRI and cluster‐failure in the individual brain |
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