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

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

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.

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

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 small-world, 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.

3D Contrast Image Reconstruction From Human Brain Activity

Several studies demonstrated that functional magnetic resonance imaging (fMRI) signals in early visual cortex can be used to reconstruct 2-dimensional (2D) visual contents. However, it remains unknown how to reconstruct 3-dimensional (3D) visual stimuli from fMRI signals in visual cortex. 3D visual stimuli contain 2D visual features and depth information. Moreover, binocular disparity is an important cue for depth perception. Thus, it is more challenging to reconstruct 3D visual stimuli than 2D visual stimuli from the fMRI signals of visual cortex. This study aimed to reconstruct 3D visual images by constructing three decoding models: contrast-decoding, disparity-decoding and contrast-disparity-decoding models, and testing these models with fMRI data from humans viewing 3D contrast images. The results revealed that the 3D contrast stimuli can be reconstructed from the visual cortex. And the early visual regions (V1, V2) showed predominant advantages in reconstructing the contrast in 3D images for the contrast-decoding model. The dorsal visual regions (V3A, V7 and MT) showed predominant advantages in decoding the disparity in 3D images for the disparity-decoding model. The combination of the early and dorsal visual regions showed predominant advantages in decoding both the contrast and disparity for the contrast-disparity-decoding model. The results suggested that the contrast and disparity in 3D images were mainly represented in the early and dorsal visual regions separately. The two visual systems may interact with each other to decode 3D-contrast images.

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

Perceiving disparities is the intuitive basis for our understanding of the physical world. Although many electrophysiology studies have revealed the disparity-tuning characteristics of the neurons in the visual areas of the macaque brain, neuron population responses to disparity processing have seldom been investigated. Many disparity studies using functional magnetic resonance imaging (fMRI) have revealed the disparity-selective visual areas in the human brain. However, it is unclear how to characterize neuron population disparity-tuning responses using fMRI technique. In the present study, we constructed three voxel-wise encoding Gabor models to predict the voxel responses to novel disparity levels and used a decoding method to identify the new disparity levels from population responses in the cortex. Among the three encoding models, the fine-coarse model (FCM) that used fine/coarse disparities to fit the voxel responses to disparities outperformed the single model and uncrossed-crossed model. Moreover, the FCM demonstrated high accuracy in predicting voxel responses in V3A complex and high accuracy in identifying novel disparities from responses in V3A complex. Our results suggest that the FCM can better characterize the voxel responses to disparities than the other two models and V3A complex is a critical visual area for representing disparity information.