A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time

Seeliger, Katja; Sommers, Rowan P.; Güçlü, Umut; Bosch, Sander; Gerven, Marcel van

doi:10.1101/687681

Cited by 13 publications

(14 citation statements)

References 24 publications

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“…One is the unprecedented scale of the dataset . NSD shares the motivation of recent ‘deep’ (or ‘precision’) neuroimaging efforts (Bellec and Boyle, 2019; Filevich et al, 2017; Gordon et al, 2017; Pinho et al, 2018; Poldrack et al, 2015; Seeliger et al, 2019) seeking to amass large amounts of data from individual subjects, as opposed to modest amounts of data on a large number of subjects. In this context, NSD is, to our knowledge, the most extensive fMRI data collection effort that has been performed to date.…”

Section: Discussionmentioning

confidence: 99%

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Allen

St-Yves

et al. 2021

Preprint

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Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.

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Section: Discussionmentioning

confidence: 99%

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Allen

St-Yves

et al. 2021

Preprint

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“…We made use of a public large fMRI dataset from single-participant responses to naturalistic video stimuli [40]. The exact experiments are described in detail in the original study [40]. In short, the participant fixated the center of the screen and watched 30 episodes of BBC's Doctor Who while their BOLD activity was measured.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…We made use of a large fMRI dataset from single-participant responses to naturalistic stimuli [38]. The exact experiments performed to obtain the data are explained in detail in the original study [38].…”

Section: Data Acquisitionmentioning

confidence: 99%

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Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity

Ambrogioni

Seeliger

et al. 2021

Preprint

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Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.

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“…Data collection was approved by the local ethical review board (CMO regio Arnhem-Nijmegen, The Netherlands, CMO code 2014-288 with amendment NL45659.091.14) and was carried out in accordance with the approved guidelines. All specifics of the data set are described in a separate manuscript accompanying the data [Seeliger et al, 2019].…”

Section: Data Acquisitionmentioning

confidence: 99%

Neural System Identification with Cortical Information Flow

Seeliger¹,

Ambrogioni²,

Güçlütürk³

et al. 2019

Preprint

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These authors contributed equally to this work. AbstractNeural information flow (NIF) is a new framework for system identification in neuroscience. It integrates population receptive field estimation, neural encoding, connectivity analysis and hemodynamic response estimation in a single differentiable model that can be trained end-to-end via stochastic gradient descent. NIF models represent neural information processing systems as a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatiotemporal location. Each tensor is coupled to the measured data specific to a brain region via lowrank observation models that can be decomposed into the spatial, temporal and feature receptive fields of a localized neuronal population. Both these observation models and the convolutional weights defining the information processing within regions and effective connectivity between regions are learned end-to-end by predicting the neural signal during sensory stimulation. We trained a NIF model on the activity of early visual areas using a large-scale fMRI dataset. We show that we can recover plausible visual representations and population receptive fields that are consistent with empirical findings.

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A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time

Cited by 13 publications

References 24 publications

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity

Neural System Identification with Cortical Information Flow

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