Microstructural properties of the vertical occipital fasciculus explain the variability in human stereoacuity.

Stereopsis is a fundamental visual function that has been studied extensively. However, it is not clear why depth discrimination (stereoacuity) varies more significantly among people than other modalities. Previous studies have reported the involvement of both dorsal and ventral visual areas in stereopsis, implying that not only neural computations in cortical areas but also the anatomical properties of white matter tracts connecting those areas can impact stereopsis. Here, we studied how human stereoacuity relates to white matter properties by combining psychophysics, diffusion MRI (dMRI), and quantitative MRI (qMRI). We performed a psychophysical experiment to measure stereoacuity and, in the same participants, we analyzed the microstructural properties of visual white matter tracts on the basis of two independent measurements, dMRI (fractional anisotropy, FA) and qMRI (macromolecular tissue volume; MTV). Microstructural properties along the right vertical occipital fasciculus (VOF), a major tract connecting dorsal and ventral visual areas, were highly correlated with measures of stereoacuity. This result was consistent for both FA and MTV, suggesting that the behavioral-structural relationship reflects differences in neural tissue density, rather than differences in the morphological configuration of fibers. fMRI confirmed that binocular disparity stimuli activated the dorsal and ventral visual regions near VOF endpoints. No other occipital tracts explained the variance in stereoacuity. In addition, the VOF properties were not associated with differences in performance on a different psychophysical task (contrast detection). These series of experiments suggest that stereoscopic depth discrimination performance is, at least in part, constrained by dorso-ventral communication through the VOF.

A relative frame of reference underlies reversed depth perception in anticorrelated random-dot stereograms.

Binocular disparity is represented by interocular cross-correlation of visual images in the striate and some extrastriate cortices. This correlation-based representation produces reversed depth perception in a binocularly anticorrelated random-dot stereogram (aRDS) when it is accompanied by an adjacent correlated RDS (cRDS). Removal of the cRDS or spatial separation between the aRDS and cRDS abolishes reversed depth perception. However, how an immediate plane supports reversed depth perception is unclear. One possible explanation is that the correlation-based representation generates reversed depth based on the relative disparity between the aRDS and cRDS rather than the absolute disparity of the aRDS. Here, we psychophysically tested this hypothesis. We found that participants perceived reversed depth in an aRDS with zero absolute disparity when it was surrounded by a cRDS with nonzero absolute disparity (i.e., nonzero relative disparity), suggesting a role of relative disparity on the depth reversal. In addition, manipulation of the absolute disparities of the central aRDS and surrounding cRDS caused depth perception to reverse with respect to the depth of the surround. Further, depth reversal persisted after swapping the locations of the two RDSs. A model of relative-disparity encoding explains all these results. We conclude that reversed depth perception in aRDSs occurs in a relative frame of reference and suggest that the visual system contains correlation-based representation that encodes relative disparity.

Reconstructing visual illusory experiences from human brain activity.

Visual illusions provide valuable insights into the brain's interpretation of the world given sensory inputs. However, the precise manner in which brain activity translates into illusory experiences remains largely unknown. Here, we leverage a brain decoding technique combined with deep neural network (DNN) representations to reconstruct illusory percepts as images from brain activity. The reconstruction model was trained on natural images to establish a link between brain activity and perceptual features and then tested on two types of illusions: illusory lines and neon color spreading. Reconstructions revealed lines and colors consistent with illusory experiences, which varied across the source visual cortical areas. This framework offers a way to materialize subjective experiences, shedding light on the brain's internal representations of the world.

Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

Achievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on neural decoding and encoding analyses where DNN unit activations and human brain activity are predicted from each other. We find that BH scores for 29 pre-trained DNNs with various architectures are negatively correlated with image recognition performance, thus indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that single-path sequential feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method may provide new ways to design DNNs in light of their representational homology to the brain.

Characterization of deep neural network features by decodability from human brain activity.

Achievements of near human-level performance in object recognition by deep neural networks (DNNs) have triggered a flood of comparative studies between the brain and DNNs. Using a DNN as a proxy for hierarchical visual representations, our recent study found that human brain activity patterns measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into DNN feature values given the same inputs. However, not all DNN features are equally decoded, indicating a gap between the DNN and human vision. Here, we present a dataset derived from DNN feature decoding analyses, which includes fMRI signals of five human subjects during image viewing, decoded feature values of DNNs (AlexNet and VGG19), and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual features were highly correlated between subjects, suggesting the systematic differences between the brain and DNNs. We hope the present dataset will contribute to revealing the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding.

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings. To date, there is no established method of predicting ordinal variables in brain decoding. In this study, we present a new algorithm, sparse ordinal logistic regression (SOLR), that combines ordinal logistic regression with Bayesian sparse weight estimation. We found that, in both simulation and analyses using real functional magnetic resonance imaging (fMRI) data, SOLR outperformed ordinal logistic regression with non-sparse regularization, indicating that sparseness leads to better decoding performance. SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs. Our results suggest that SOLR provides a principled and effective method of decoding ordinal variables.