Summary Humans can see and name thousands of distinct object and action categories, so it is unlikely that each category is represented in a distinct brain area. A more efficient scheme would be to represent categories as locations in a continuous semantic space mapped smoothly across the cortical surface. To search for such a space, we used functional magnetic resonance imaging (fMRI) to measure human brain activity evoked by natural movies. We then used voxel-wise models to examine the cortical representation of 1705 object and action categories. The first few dimensions of the underlying semantic space were recovered from the fit models by principal components analysis. Projection of the recovered semantic space onto cortical flat maps shows that semantic selectivity is organized into smooth gradients that cover much of visual and non-visual cortex. Furthermore, both the recovered semantic space and the cortical organization of the space are shared across different individuals.

Summary Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2], and can form the basis for brain decoding devices [3–5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns, and have shown that it is possible to reconstruct these images from brain activity measurements [6–8]. However, blood oxygen level dependent (BOLD) signals measured using fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcome this limitation. Our motion-energy model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipito-temporal visual cortex of human subjects who passively watched natural movies, and fit the encoding model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent moving stimuli. To demonstrate the power of our approach we also constructed a Bayesian decoder [8], by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of natural movies, capturing the spatio-temporal structure of the viewed movie. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.

Over the past decade fMRI researchers have developed increasingly sensitive techniques for analyzing the information represented in BOLD activity. The most popular of these techniques is linear classification, a simple technique for decoding information about experimental stimuli or tasks from patterns of activity across an array of voxels. A more recent development is the voxel-based encoding model, which describes the information about the stimulus or task that is represented in the activity of single voxels. Encoding and decoding are complementary operations: encoding uses stimuli to predict activity while decoding uses activity to predict information about stimuli. However, in practice these two operations are often confused, and their respective strengths and weaknesses have not been made clear. Here we use the concept of a linearizing feature space to clarify the relationship between encoding and decoding. We show that encoding and decoding operations can both be used to investigate some of the most common questions about how information is represented in the brain. However, focusing on encoding models offers two important advantages over decoding. First, an encoding model can in principle provide a complete functional description of a region of interest, while a decoding model can provide only a partial description. Second, while it is straightforward to derive an optimal decoding model from an encoding model it is much more difficult to derive an encoding model from a decoding model. We propose a systematic modeling approach that begins by estimating an encoding model for every voxel in a scan and ends by using the estimated encoding models to perform decoding.

No abstract

Little is known about how attention changes the cortical representation of sensory information in humans. Based on neurophysiological evidence, we hypothesized that attention causes tuning changes to expand the representation of attended stimuli at the cost of unattended stimuli. To investigate this issue we used functional MRI (fMRI) to measure how semantic representation changes when searching for different object categories in natural movies. We find that many voxels across occipito-temporal and fronto-parietal cortex shift their tuning toward the attended category. These tuning shifts expand the representation of the attended category and of semantically-related but unattended categories, and compress the representation of categories semantically-dissimilar to the target. Attentional warping of semantic representation occurs even when the attended category is not present in the movie, thus the effect is not a target-detection artifact. These results suggest that attention dynamically alters visual representation to optimize processing of behaviorally relevant objects during natural vision.

Highly ordered TiO2 nanotube array prepared by a potentiostatic anodization shows a considerable potential for improving the transport of the photogenerated electrons in the TiO2 film, since the ordered architecture can provide a unidirectional electric channel and reduce the grain boundaries. Here, we report on the application of highly ordered TiO2 nanotube arrays with different lengths for the photoelectrocatalytic degradation of phenol. The lengths of the nanotube arrays can be controlled by the electrolyte media, anodization time, or both. The photoelectrocatalytic activity shows a dependence on the length of the nanotube arrays. Under 3.1 mW/cm2 irradiance of ultraviolet light, a short nanotube array shows better photoelectrocatalytic activity than a long nanotube array, which can be explained by the reduced recombination effects. When compared with a P25 TiO2 particulate film with similar thickness and geometric area, the nanotube array shows a stronger attachment to the parent titanium substrate and a better photoelectrocatalytic activity for phenol degradation owing to the improved electron transport and reduced charge recombination. This superior electron transport is further supported by the remarkably enhanced anodic photocurrent response in the degradation of phenol.

We report herein the preparation and UV-stimulated wettability conversion of superhydrophobic TiO2 surfaces, as well as the preparation of superhydrophilic−superhydrophobic patterns by use of UV irradiation through a photomask. A CF4 plasma was used to roughen smooth TiO2 sol−gel films to produce a nanocolumnar morphology, and subsequent hydrophobic modification with octadecylphosphonic acid (ODP) rendered the roughened surfaces superhydrophobic. The superhydrophobic properties of these surfaces were evaluated by both static and dynamic water contact angle (CA) measurements. It was found that the surface morphology of the TiO2 film, which was dependent on the etching time, has a great influence on the observed superhydrophobic properties. The nanocolumnar surface morphology exhibited large water CA and small contact angle hysteresis (CAH); this is discussed in terms of the Wenzel equation and the Cassie−Baxter equation. Under low-intensity UV illumination (1 mW cm-2), the superhydrophobic TiO2 surface underwent a gradual decrease of water CA and finally became superhydrophilic, due to photocatalytic decomposition of the ODP monolayer. Readsorption of ODP molecules led to the recovery of the superhydrophobic state. This UV-stimulated wettability conversion was employed to prepare superhydrophilic stripes (50 and 500 μm wide) on a superhydrophobic TiO2 surface. The pattern was able to guide water condensation, as well as the evaporation of a polystyrene microsphere suspension, due to the extremely large wettability contrast between superhydrophobic and superhydrophilic areas.

Highly ordered TiO2 nanotube array prepared by electrochemical anodization generates considerable interest as a practical air purifier, since a nanotube array can form a TiO2 film with a porous surface and straight gas diffusion channel, simultaneously reserving enough geometric thickness. Here, we reported on the application of highly ordered TiO2 nanotube arrays with different lengths for degradation of gaseous acetaldehyde pollutants in air. The results showed that increasing the lengths of nanotube arrays within a certain range could significantly improve the degradation rate of acetaldehyde molecules. The main product of acetaldehyde degradation was detected to be CO2, which indicated that the mineralization of acetaldehyde molecules was the major process in this photocatalytic reaction. When compared with a P25 TiO2 nanoparticulate film with similar thickness and geometric area, in the initial degradation of acetaldehyde, the nanotube array did not show obvious superiority. However, in the subsequent degradation, the nanotube array demonstrated an enhanced photocatalytic activity. It was suggested that this enhancement resulted from the special infrastructure of the nanotube array, which was favorable for the diffusion of intermediates and the reduced deactivation of photocatalyst in the photocatalytic reaction.