Disparity level identification using the voxel‐wise Gabor model of fMRI data

Li, Yuan; Hou, Chunping; Yao, Li; Zhang, Chuncheng; Zheng, Hongna; Zhang, Jiacai; Long, Zhiling

doi:10.1002/hbm.24547

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…It can be seen that the change of the brain network scales affect the understanding of the brain functional networks, which will be a very important and challenging direction. With the development of the research into the voxel level, the computing time and complexity will increase greatly, and the existing methods based on topological statistical features will be significantly restricted (Li et al 2019).…”

Section: Brain Network Sizementioning

confidence: 99%

Brain functional network modeling and analysis based on fMRI: a systematic review

et al. 2020

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In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as smallworld, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions. Keywords Brain functional networks Á Complex network Á Topological properties Á Neurological and psychiatric diseases Abbreviations AAL Anatomical automatic labeling BOLD Blood oxygenation level dependent CAD Computer aided diagnosis EEG Electroencephalogram fMRI Functional magnetic resonance imaging ICA Independent component analysis MEG Magnetoencephalography PCA Principal component analysis ROI Region of interest SVD Singular value decomposition SVM Support vector machine SPM statistical parametric mapping toolkit TSCI Traumatic complete spinal cord injury. WHO World Health Organization

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Section: Brain Network Sizementioning

confidence: 99%

Brain functional network modeling and analysis based on fMRI: a systematic review

et al. 2020

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“…Binocular stereopsis underlies our perceptual experience of stereoscopic depth and visual three-dimensional structure. Stereopsis is supported by a set of neural mechanisms for disparity selectivity and binocular integration that are distributed across multiple cortical regions in the human visual cortex (Backus et al 2001 ; Bridge and Parker 2007 ; Preston et al 2008 ; Ip et al 2014 ; Goncalves et al 2015 ; Li et al 2019 ) and characterised by selective responses to specific stimulus features, such as absolute and relative disparity, surface curvature, slant, or separation in depth (Parker 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Human primary visual cortex shows larger population receptive fields for binocular disparity-defined stimuli

2021

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The visual perception of 3D depth is underpinned by the brain’s ability to combine signals from the left and right eyes to produce a neural representation of binocular disparity for perception and behaviour. Electrophysiological studies of binocular disparity over the past 2 decades have investigated the computational role of neurons in area V1 for binocular combination, while more recent neuroimaging investigations have focused on identifying specific roles for different extrastriate visual areas in depth perception. Here we investigate the population receptive field properties of neural responses to binocular information in striate and extrastriate cortical visual areas using ultra-high field fMRI. We measured BOLD fMRI responses while participants viewed retinotopic mapping stimuli defined by different visual properties: contrast, luminance, motion, correlated and anti-correlated stereoscopic disparity. By fitting each condition with a population receptive field model, we compared quantitatively the size of the population receptive field for disparity-specific stimulation. We found larger population receptive fields for disparity compared with contrast and luminance in area V1, the first stage of binocular combination, which likely reflects the binocular integration zone, an interpretation supported by modelling of the binocular energy model. A similar pattern was found in region LOC, where it may reflect the role of disparity as a cue for 3D shape. These findings provide insight into the binocular receptive field properties underlying processing for human stereoscopic vision.

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“…Binocular stereopsis underlies our perceptual experience of stereoscopic depth and visual three-dimensional structure. Stereopsis is supported by a set of neural mechanisms for disparity selectivity and binocular integration that are distributed across multiple cortical regions in the human visual cortex (Backus et al 2001; Bridge and Parker 2007; Preston et al 2008; Ip et al 2014; Goncalves et al 2015; Li et al 2019) and characterized by selective responses to specific stimulus features, such as absolute and relative disparity, surface curvature, slant, or separation in depth (Parker 2007).…”

Section: Introductionmentioning

confidence: 99%

Human primary visual cortex shows larger population receptive fields for binocular disparity-defined stimuli

Alvarez

Parker

Bridge

2021

Preprint

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The visual perception of 3D depth is underpinned by the brain's ability to combine signals from the left and right eyes to produce a neural representation of binocular disparity for perception and behavior. Electrophysiological studies of binocular disparity over the past two decades have investigated the computational role of neurons in area V1 for binocular combination, while more recent neuroimaging investigations have focused on identifying specific roles for different extrastriate visual areas in depth perception. Here we investigate the neural population receptive field properties of responses to binocular information in striate and extrastriate cortical visual areas using ultra-high field fMRI. We measured BOLD fMRI responses while participants viewed retinotopic-mapping stimuli defined by different visual properties: contrast, luminance, motion, correlated and anti-correlated stereoscopic disparity. By fitting each condition with a population receptive field model, we were able to compare quantitatively the size of the population receptive field for disparity-defined vs. not disparity-defined stimulation conditions. We found larger population receptive fields for disparity compared to the contrast and luminance stimuli in area V1, the first stage of binocular combination, which likely reflects the binocular integration zone, an interpretation supported by modelling of the binocular energy model. A similar pattern was found in region LOC, where it may reflect the role of disparity as a cue for 3D shape. These findings provide insight into the binocular receptive field properties underlying processing for human stereoscopic vision.

show abstract

Disparity level identification using the voxel‐wise Gabor model of fMRI data

Cited by 7 publications

References 59 publications

Brain functional network modeling and analysis based on fMRI: a systematic review

Brain functional network modeling and analysis based on fMRI: a systematic review

Human primary visual cortex shows larger population receptive fields for binocular disparity-defined stimuli

Human primary visual cortex shows larger population receptive fields for binocular disparity-defined stimuli

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