qPRF: A system to accelerate population receptive field modeling
BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 306; p. 120994 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.02.2025
Elsevier Limited Elsevier |
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| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2024.120994 |
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| Abstract | BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 70.2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications.
•We describe a system to perform PRF modeling up to 1,500 times faster than others.•The system achieves equivalent goodness-of-fit as others.•A pre-computed tree and similarity-based search strategy underlie the acceleration. |
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| AbstractList | BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 70.2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications. BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 70.2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications. •We describe a system to perform PRF modeling up to 1,500 times faster than others.•The system achieves equivalent goodness-of-fit as others.•A pre-computed tree and similarity-based search strategy underlie the acceleration. BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex ( Dumoulin and Wandell, 2008 ). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor > 1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package ( Kay et al., 2013 ) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013) . The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R 2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% ( R 2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R 2 on 70.2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications. BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor > 1 , 000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R 2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% ( R 2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R 2 on 70.2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications. BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF ("quick PRF"), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 70.2% of vertices. We also assess the qPRF method's model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications.BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF ("quick PRF"), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 70.2% of vertices. We also assess the qPRF method's model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications. BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF ("quick PRF"), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R on 70.2% of vertices. We also assess the qPRF method's model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications. |
| ArticleNumber | 120994 |
| Author | Waz, Sebastian Wang, Yalin Lu, Zhong-Lin |
| AuthorAffiliation | b School of Computing and Augmented Intelligence, Arizona State University, 699 S. Mill Avenue, Tempe, 85281, AZ, USA d NYU-ECNU Institute of Brain and Cognitive Science, 3663 Zhongshan Road North, Putuo District, 200062, Shanghai, China c Division of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New District, 200124, Shanghai, China a Center for Neural Science, New York University, 4 Washington Place, NY, 10003, NY, USA |
| AuthorAffiliation_xml | – name: c Division of Arts and Sciences, NYU Shanghai, 567 West Yangsi Road, Pudong New District, 200124, Shanghai, China – name: d NYU-ECNU Institute of Brain and Cognitive Science, 3663 Zhongshan Road North, Putuo District, 200062, Shanghai, China – name: a Center for Neural Science, New York University, 4 Washington Place, NY, 10003, NY, USA – name: b School of Computing and Augmented Intelligence, Arizona State University, 699 S. Mill Avenue, Tempe, 85281, AZ, USA |
| Author_xml | – sequence: 1 givenname: Sebastian surname: Waz fullname: Waz, Sebastian organization: Center for Neural Science, New York University, 4 Washington Place, NY, 10003, NY, USA – sequence: 2 givenname: Yalin orcidid: 0000-0002-6241-735X surname: Wang fullname: Wang, Yalin organization: School of Computing and Augmented Intelligence, Arizona State University, 699 S. Mill Avenue, Tempe, 85281, AZ, USA – sequence: 3 givenname: Zhong-Lin orcidid: 0000-0002-7295-727X surname: Lu fullname: Lu, Zhong-Lin email: zhonglin@nyu.edu organization: Center for Neural Science, New York University, 4 Washington Place, NY, 10003, NY, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39761863$$D View this record in MEDLINE/PubMed |
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| Keywords | Retinotopic mapping Data structures Population receptive field model Vision Optimization |
| Language | English |
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| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2025 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
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| Snippet | BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell,... BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex ( Dumoulin and Wandell,... |
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| SubjectTerms | Brain Connectome - methods Data structures Datasets Estimates Humans Magnetic Resonance Imaging - methods Models, Neurological Neurosurgery Optimization Population receptive field model Receptive field Retinotopic mapping Time series Vision |
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| Title | qPRF: A system to accelerate population receptive field modeling |
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