Bayesian population receptive field modeling in human somatosensory cortex

Puckett, Alexander M.; Bollmann, Saskia; Junday, Keerat; Barth, Markus; Cunnington, Ross

doi:10.1101/577981

Cited by 13 publications

(42 citation statements)

References 77 publications

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“…This showed that there was spatial agreement between the methods, but crucially that iMP could also estimate digit overlap ( Da Rocha Amaral et al., 2020 ). Furthermore, a study by Puckett et al. (2020) applied a Bayesian variant of population receptive field mapping ( Zeidman et al., 2018 ) to estimate receptive field size and centre of mass in S1, and showed digit preference and overlap ( Puckett et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, a study by Puckett et al. (2020) applied a Bayesian variant of population receptive field mapping ( Zeidman et al., 2018 ) to estimate receptive field size and centre of mass in S1, and showed digit preference and overlap ( Puckett et al., 2020 ). These recent advances in extracting digit size and location from travelling wave data will prove useful in future, as travelling wave designs are much faster at digit mapping than event related designs ( Besle et al., 2013 ).…”

Section: Discussionmentioning

confidence: 99%

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A probabilistic atlas of finger dominance in the primary somatosensory cortex

et al. 2020

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With the advent of ultra-high field (7T), high spatial resolution functional MRI (fMRI) has allowed the differentiation of the cortical representations of each of the digits at an individual-subject level in human primary somatosensory cortex (S1). Here we generate a probabilistic atlas of the contralateral SI representations of the digits of both the left and right hand in a group of 22 right-handed individuals. The atlas is generated in both volume and surface standardised spaces from somatotopic maps obtained by delivering vibrotactile stimulation to each distal phalangeal digit using a travelling wave paradigm. Metrics quantify the likelihood of a given position being assigned to a digit (full probability map) and the most probable digit for a given spatial location (maximum probability map). The atlas is validated using a leave-one-out cross validation procedure. Anatomical variance across the somatotopic map is also assessed to investigate whether the functional variability across subjects is coupled to structural differences. This probabilistic atlas quantifies the variability in digit representations in healthy subjects, finding some quantifiable separability between digits 2, 3 and 4, a complex overlapping relationship between digits 1 and 2, and little agreement of digit 5 across subjects. The atlas and constituent subject maps are available online for use as a reference in future neuroimaging studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A probabilistic atlas of finger dominance in the primary somatosensory cortex

et al. 2020

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“…Apart from characterizing the properties of voxels in response to external stimulation, the pRF modeling approach has also been extended to study resting state connectivity ( Gravel et al, 2014 ). Outside the study of visual areas, pRF modeling approaches have been successfully applied to map the spatial organization of the somatosensory cortex ( Puckett et al, 2020 ) as well as the preference and selectivity (i.e., tuning) to sound frequency in auditory cortical areas ( Thomas et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Reliability of Population Receptive Field Size Estimates Using fMRI

et al. 2020

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In functional MRI (fMRI), population receptive field (pRF) models allow a quantitative description of the response as a function of the features of the stimuli that are relevant for each voxel. The most popular pRF model used in fMRI assumes a Gaussian shape in the features space (e.g., the visual field) reducing the description of the voxel's pRF to the Gaussian mean (the pRF preferred feature) and standard deviation (the pRF size). The estimation of the pRF mean has been proven to be highly reliable. However, the estimate of the pRF size has been shown not to be consistent within and between subjects. While this issue has been noted experimentally, here we use an optimization theory perspective to describe how the inconsistency in estimating the pRF size is linked to an inherent property of the Gaussian pRF model. When fitting such models, the goodness of fit is less sensitive to variations in the pRF size than to variations in the pRF mean. We also show how the same issue can be considered from a bias-variance perspective. We compare different estimation procedures in terms of the reliability of their estimates using simulated and real fMRI data in the visual (using the Human Connectome Project database) and auditory domain. We show that, the reliability of the estimate of the pRF size can be improved considering a linear combination of those pRF models with similar goodness of fit or a permutation based approach. This increase in reliability of the pRF size estimate does not affect the reliability of the estimate of the pRF mean and the prediction accuracy.

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“…This 'population receptive field' method (Dumoulin and Wandell, 2008;Wandell et al, 2007;Winawer, 2015, 2010) has rapidly become a popular method to map the functional organization of the human brain. In addition to describing the retinotopic organization of retinal activation in cortical and subcortical brain areas, the method has been used to map the cortical representation of other stimulus features such as tonotopy (Thomas et al, 2015), numerosity (Harvey et al, 2015), tactile sensations (Puckett et al, 2020), and visual timing (Harvey et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Early studies used phase-encoding with ‘rotating wedge’ and ‘expanding or contracting ring’ stimuli to identify RF position (Engel, 2012; Engel et al, 1994; Sereno et al, 1995), while later studies increasingly used the ‘population receptive field’ (pRF) method that estimates RF-size in addition to position (Dumoulin and Wandell, 2008; Wandell et al, 2007; Wandell and Winawer, 2015, 2010). The method is popular and has been used to map a range of visual and cognitive functions (Binda et al, 2018; Ekman et al, 2020; Harvey et al, 2020, 2015; He et al, 2019; Hughes et al, 2019; Mo et al, 2017; Poltoratski et al, 2019; Poltoratski and Tong, 2020; Puckett et al, 2020; Shao et al, 2013; Shen et al, 2020; Silson et al, 2018; Stoll et al, 2020; Thomas et al, 2015; Welbourne et al, 2018; Zuiderbaan et al, 2017), dysfunctions (Ahmadi et al, 2020; Alvarez et al, 2020; Best et al, 2019; Dumoulin and Knapen, 2018; Green et al, 2019; Schwarzkopf et al, 2014), mechanisms of brain development (Dekker et al, 2019), cortical evolution (Keliris et al, 2019; Kolster et al, 2014; Zhu and Vanduffel, 2019), and information transfer across different brain areas (Haak et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex

Klink

Chen

Vanduffel

et al. 2020

Preprint

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Population receptive field (pRF) modeling is a popular method to map the retinotopic organization of the human brain with fMRI. While BOLD-based pRF-maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they truly represent. We address this question with whole-brain fMRI and large-scale neurophysiological recordings in awake non-human primates. Several pRF-models were independently fit to the BOLD signal, multi-unit spiking activity (MUA) and local field potential (LFP) power in distinct frequency bands. Our results provide a retinotopic characterization of cortical and subcortical areas, suggest brain-wide compressive (i.e., sublinear) spatial summation, and demonstrate a visually tuned deactivation of default mode network nodes. Cross-signal analysis of pRF-map structure (eccentricity-size relation) indicates that the neural underpinnings of BOLD-pRFs are area-specific. In V1, BOLD-pRFs mirror MUA, while in V4 they are more similar to the tuning of the gamma LFP-power.

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Bayesian population receptive field modeling in human somatosensory cortex

Abstract: 21Somatosensation is fundamental to our ability to sense our body and interact with the 22

Cited by 13 publications

References 77 publications

A probabilistic atlas of finger dominance in the primary somatosensory cortex

A probabilistic atlas of finger dominance in the primary somatosensory cortex

Investigating the Reliability of Population Receptive Field Size Estimates Using fMRI

Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex

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