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Soch J, Görgen K, Heinzle J, Haynes JD. A tightly controlled fMRI dataset for receptive field mapping in human visual cortex. Data Brief 2023; 47:109018. [PMID: 36936634 PMCID: PMC10018042 DOI: 10.1016/j.dib.2023.109018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 03/04/2023] Open
Abstract
Four right-handed, healthy subjects participated in a visual stimulation experiment. Subjects were viewing a dartboard-shaped flickering checkerboard stimulus, divided into 4 rings and 12 segments, defining 48 sectors in the visual field. Local contrast in each sector was continuously varying across four levels and updated every 3 s. To maintain fixation, subjects had to respond to a stimulus at the center of the visual field. During the entire experiment, in which subjects performed 8 runs, each consisting of 100 trials, brain activity was measured with functional magnetic resonance imaging (MRI). Using a 3-T Siemens Trio MRI scanner, 220 echo-planar images were acquired in each run, with a repetition time of 1.5 s and voxel size of 3 x 3 x 3 mm. The dataset is publicly available from OpenNeuro and additionally includes region of interest maps for visual areas V1 to V4, left and right, obtained from another retinotopic mapping experiment. As such, the dataset allows for accurate mapping of receptive fields and their properties across several stages of human visual cortex.
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Affiliation(s)
- Joram Soch
- Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Sauerbruchweg 4, 10117 Berlin, Germany
- Berlin Center for Computational Neuroscience, Philippstraße 13, Haus 6, 10115 Berlin, Germany
- German Center for Neurodegenerative Diseases, Von-Siebold-Straße 3A, 37075 Göttingen, Germany
- Corresponding author. @JoramSoch
| | - Kai Görgen
- Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Sauerbruchweg 4, 10117 Berlin, Germany
- Berlin Center for Computational Neuroscience, Philippstraße 13, Haus 6, 10115 Berlin, Germany
- EXC Science of Intelligence, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
| | - Jakob Heinzle
- Berlin Center for Computational Neuroscience, Philippstraße 13, Haus 6, 10115 Berlin, Germany
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zürich, Switzerland
| | - John-Dylan Haynes
- Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Sauerbruchweg 4, 10117 Berlin, Germany
- Berlin Center for Computational Neuroscience, Philippstraße 13, Haus 6, 10115 Berlin, Germany
- Berlin School of Mind and Brain, Humboldt University of Berlin, Luisenstraße 56, Haus 1, 10117 Berlin, Germany
- Department of Psychology, Humboldt University of Berlin, Germany, Wolfgang Köhler-Haus, Rudower Chaussee 18, 12489 Berlin
- EXC Science of Intelligence, Technical University of Berlin, Marchstr. 23, 10587 Berlin, Germany
- CRC Volition and Cognitive Control, Technical University of Dresden, Zellescher Weg 17, 01069 Dresden, Germany
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Prince JS, Charest I, Kurzawski JW, Pyles JA, Tarr MJ, Kay KN. Improving the accuracy of single-trial fMRI response estimates using GLMsingle. eLife 2022; 11:77599. [PMID: 36444984 PMCID: PMC9708069 DOI: 10.7554/elife.77599] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 10/15/2022] [Indexed: 11/30/2022] Open
Abstract
Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.
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Affiliation(s)
- Jacob S Prince
- Department of Psychology, Harvard University, Cambridge, United States
| | - Ian Charest
- Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom.,cerebrUM, Département de Psychologie, Université de Montréal, Montréal, Canada
| | - Jan W Kurzawski
- Department of Psychology, New York University, New York, United States
| | - John A Pyles
- Center for Human Neuroscience, Department of Psychology, University of Washington, Seattle, United States
| | - Michael J Tarr
- Department of Psychology, Neuroscience Institute, Carnegie Mellon University, Pittsburgh, United States
| | - Kendrick N Kay
- Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, United States
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Soch J, Richter A, Schütze H, Kizilirmak JM, Assmann A, Knopf L, Raschick M, Schult A, Maass A, Ziegler G, Richardson-Klavehn A, Düzel E, Schott BH. Bayesian model selection favors parametric over categorical fMRI subsequent memory models in young and older adults. Neuroimage 2021; 230:117820. [PMID: 33524573 DOI: 10.1016/j.neuroimage.2021.117820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 01/25/2021] [Indexed: 01/10/2023] Open
Abstract
Subsequent memory paradigms allow to identify neural correlates of successful encoding by separating brain responses as a function of memory performance during later retrieval. In functional magnetic resonance imaging (fMRI), the paradigm typically elicits activations of medial temporal lobe, prefrontal and parietal cortical structures in young, healthy participants. This categorical approach is, however, limited by insufficient memory performance in older and particularly memory-impaired individuals. A parametric modulation of encoding-related activations with memory confidence could overcome this limitation. Here, we applied cross-validated Bayesian model selection (cvBMS) for first-level fMRI models to a visual subsequent memory paradigm in young (18-35 years) and older (51-80 years) adults. Nested cvBMS revealed that parametric models, especially with non-linear transformations of memory confidence ratings, outperformed categorical models in explaining the fMRI signal variance during encoding. We thereby provide a framework for improving the modeling of encoding-related activations and for applying subsequent memory paradigms to memory-impaired individuals.
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Affiliation(s)
- Joram Soch
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany; Bernstein Center for Computational Neuroscience (BCCN), Berlin, Germany.
| | - Anni Richter
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany
| | - Hartmut Schütze
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | | | - Anne Assmann
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Lea Knopf
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Matthias Raschick
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Annika Schult
- Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany
| | | | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Otto von Guericke University, Medical Faculty, Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany; Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.
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