ContrAttNet: Contribution and attention approach to multivariate time-series data imputation.

The imputation of missing values in multivariate time-series data is a basic and popular data processing technology. Recently, some studies have exploited Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs) to impute/fill the missing values in multivariate time-series data. However, when faced with datasets with high missing rates, the imputation error of these methods increases dramatically. To this end, we propose a neural network model based on dynamic contribution and attention, denoted as ContrAttNet. ContrAttNet consists of three novel modules: feature attention module, iLSTM (imputation Long Short-Term Memory) module, and 1D-CNN (1-Dimensional Convolutional Neural Network) module. ContrAttNet exploits temporal information and spatial feature information to predict missing values, where iLSTM attenuates the memory of LSTM according to the characteristics of the missing values, to learn the contributions of different features. Moreover, the feature attention module introduces an attention mechanism based on contributions, to calculate supervised weights. Furthermore, under the influence of these supervised weights, 1D-CNN processes the time-series data by treating them as spatial features. Experimental results show that ContrAttNet outperforms other state-of-the-art models in the missing value imputation of multivariate time-series data, with average 6% MAPE and 9% MAE on the benchmark datasets.

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation.

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels for the clinical diagnosis of fundus and coronary artery disease. However, traditional convolutional neural networks (CNNs) are confined to learning local vessel features, making it challenging to capture the graph structural information and fail to perceive the global context of vessels. Therefore, we propose a novel graph neural network-guided vision transformer enhanced network (G2ViT) for vessel segmentation. G2ViT skillfully orchestrates the Convolutional Neural Network, Graph Neural Network, and Vision Transformer to enhance comprehension of the entire graphical structure of blood vessels. To achieve deeper insights into the global graph structure and higher-level global context cognizance, we investigate a graph neural network-guided vision transformer module. This module constructs graph-structured representation in an unprecedented manner using the high-level features extracted by CNNs for graph reasoning. To increase the receptive field while ensuring minimal loss of edge information, G2ViT introduces a multi-scale edge feature attention module (MEFA), leveraging dilated convolutions with different dilation rates and the Sobel edge detection algorithm to obtain multi-scale edge information of vessels. To avoid critical information loss during upsampling and downsampling, we design a multi-level feature fusion module (MLF2) to fuse complementary information between coarse and fine features. Experiments on retinal vessel datasets (DRIVE, STARE, CHASE_DB1, and HRF) and coronary angiography datasets (DCA1 and CHUAC) indicate that the G2ViT excels in robustness, generality, and applicability. Furthermore, it has acceptable inference time and computational complexity and presents a new solution for blood vessel segmentation.

A Multi-Stage Feature Aggregation and Structure Awareness Network for Concrete Bridge Crack Detection.

One of the most significant problems affecting a concrete bridge's safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolutional methods with square kernels struggle to capture crack features effectively, fail to perceive the long-range dependencies between crack regions, and have weak suppression ability for background noises, leading to low detection precision of bridge cracks. To address this problem, a multi-stage feature aggregation and structure awareness network (MFSA-Net) for pixel-level concrete bridge crack detection is proposed in this paper. Specifically, in the coding stage, a structure-aware convolution block is proposed by combining square convolution with strip convolution to perceive the linear structure of concrete bridge cracks. Square convolution is used to capture detailed local information. In contrast, strip convolution is employed to interact with the local features to establish the long-range dependence relationship between discrete crack regions. Unlike the self-attention mechanism, strip convolution also suppresses background interference near crack regions. Meanwhile, the feature attention fusion block is presented for fusing features from the encoder and decoder at the same stage, which can sharpen the edges of concrete bridge cracks. In order to fully utilize the shallow detail features and deep semantic features, the features from different stages are aggregated to obtain fine-grained segmentation results. The proposed MFSA-Net was trained and evaluated on the publicly available concrete bridge crack dataset and achieved average results of 73.74%, 77.04%, 75.30%, and 60.48% for precision, recall, F1 score, and IoU, respectively, on three typical sub-datasets, thus showing optimal performance in comparison with other existing methods. MFSA-Net also gained optimal performance on two publicly available concrete pavement crack datasets, thereby indicating its adaptability to crack detection across diverse scenarios.

Dissociable neuronal substrates of visual feature attention and working memory.

Attention and working memory (WM) are distinct cognitive functions, yet given their close interactions, it is often assumed that they share the same neuronal mechanisms. We show that in macaques performing a WM-guided feature attention task, the activity of most neurons in areas middle temporal (MT), medial superior temporal (MST), lateral intraparietal (LIP), and posterior lateral prefrontal cortex (LPFC-p) displays attentional modulation or WM coding and not both. One area thought to play a role in both functions is LPFC-p. To test this, we optogenetically inactivated LPFC-p bilaterally during different task periods. Attention period inactivation reduced attentional modulation in LPFC-p, MST, and LIP neurons and impaired task performance. In contrast, WM period inactivation did not affect attentional modulation or performance and minimally affected WM coding. Our results suggest that feature attention and WM have dissociable neuronal substrates and that LPFC-p plays a critical role in feature attention, but not in WM.

TSANet: multibranch attention deep neural network for classifying tactile selective attention in brain-computer interfaces.

Brain-computer interfaces (BCIs) enable communication between the brain and a computer and electroencephalography (EEG) has been widely used to implement BCIs because of its high temporal resolution and noninvasiveness. Recently, a tactile-based EEG task was introduced to overcome the current limitations of visual-based tasks, such as visual fatigue from sustained attention. However, the classification performance of tactile-based BCIs as control signals is unsatisfactory. Therefore, a novel classification approach is required for this purpose. Here, we propose TSANet, a deep neural network, that uses multibranch convolutional neural networks and a feature-attention mechanism to classify tactile selective attention (TSA) in a tactile-based BCI system. We tested TSANet under three evaluation conditions, namely, within-subject, leave-one-out, and cross-subject. We found that TSANet achieved the highest classification performance compared with conventional deep neural network models under all evaluation conditions. Additionally, we show that TSANet extracts reasonable features for TSA by investigating the weights of spatial filters. Our results demonstrate that TSANet has the potential to be used as an efficient end-to-end learning approach in tactile-based BCIs. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00309-4.

CVANet: Cascaded visual attention network for single image super-resolution.

Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet.

MFA-ICPS: Semi-supervised medical image segmentation with improved cross pseudo supervision and multi-dimensional feature attention.

BACKGROUND: In the medical field, medical image segmentation plays a pivotal role in facilitating disease evaluation and supporting treatment decision-making for doctors. Recently, deep learning methods have been employed in the field of medical image segmentation. However, the manual annotation of a large number of reliable labels is a costly and time-consuming process. PURPOSE: To address this challenge, a semi-supervised learning framework is required to alleviate the burden of reliable labeling and enhance segmentation accuracy in challenging areas of medical images. METHODS: Therefore, this paper presents MFA-ICPS framework, a semi-supervised learning framework based on the improved cross pseudo supervision (ICPS) and multi-dimensional feature attention (MFA) modules. Medical images inevitably contain some noise that may affect the segmentation accuracy, so the proposed framework addresses this challenge by introducing noise disturbance, combining ICPS and MFA modules, and using pseudo-segmentation maps and MFA maps to maintain the consistency at both the output and feature levels. RESULTS: In the experiments, MFA-ICPS framework accurately obtains the following performances on the left atrial dataset: Dice, Jaccard, 95HD, and ASD values are 90.89 % $90.89\%$ , 83.40 % $83.40\%$ , 6.00 and 1.94 mm, respectively. And on the pancreas-CT dataset, the following performances are accurately obtained: Dice, Jaccard, 95HD, and ASD values are 79.55 % $79.55\%$ , 66.87 % $66.87\%$ , 7.67 and 1.65 mm, respectively. CONCLUSIONS: The segmentation performance of MFA-ICPS framework on different medical datasets demonstrates its remarkable capability to significantly enhance medical image segmentation.

Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.

Background and Objective: Parkinson's disease (PD) is a progressive neurological condition that manifests motor and non-motor symptoms. Early in the course of the disease, PD patients frequently experience vocal difficulties. In the beginning, preprocessing procedures were used with multi-focus image fusion to enhance the quality of input images. It is essential to diagnose and treat PD early to ensure that patients live healthy and productive lives. Methods: Tremors, rigidity in the muscles, slow movement, difficulty balance, and other psychological symptoms are some of the disease's symptoms. One of the critical mechanisms supporting PD identification and assessment is the dynamics of handwritten records. Several machine-learning techniques have been researched for the early detection of this disease. Yet the main problem with most of these manual feature extraction methods is their poor performance and accuracy. Results: This cannot be acceptable when discovering such a chronic condition. For this purpose, a powerful deep learning model is suggested to help with the early diagnosis of Parkinson's disease. Therefore, we proposed MobileNetV3-based classification. To enhance the classification performances even more, the MobileNetV3-based approach was optimized by the Improved Dwarf Mongoose Optimization algorithm (IDMO). Conclusion: The Pyramid channel-based feature attention network (PCFAN) chooses the critical features. The efficiency of the approaches is tested using the PPMI and NTUA datasets. Our proposed approach obtains 99.34% accuracy, 98.53% sensitivity, 97.78% specificity, and 99.12% F-score compared to previous methods.

Visual attention to features and space in mice using reverse correlation.

Visual attention allows the brain to evoke behaviors based on the most important visual features. Mouse models offer immense potential to gain a circuit-level understanding of this phenomenon, yet how mice distribute attention across features and locations is not well understood. Here, we describe a new approach to address this limitation by training mice to detect weak vertical bars in a background of dynamic noise while spatial cues manipulate their attention. By adapting a reverse-correlation method from human studies, we linked behavioral decisions to stimulus features and locations. We show that mice deployed attention to a small rostral region of the visual field. Within this region, mice attended to multiple features (orientation, spatial frequency, contrast) that indicated the presence of weak vertical bars. This attentional tuning grew with training, multiplicatively scaled behavioral sensitivity, approached that of an ideal observer, and resembled the effects of attention in humans. Taken together, we demonstrate that mice can simultaneously attend to multiple features and locations of a visual stimulus.

Meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network for imbalanced breast cancer histopathological image classification.

Breast cancer histopathological image automatic classification can reduce pathologists workload and provide accurate diagnosis. However, one challenge is that empirical datasets are usually imbalanced, resulting in poorer classification quality compared with conventional methods based on balanced datasets. The recently proposed bilateral branch network (BBN) tackles this problem through considering both representation and classifier learning to improve classification performance. We firstly apply bilateral sampling strategy to imbalanced breast cancer histopathological image classification and propose a meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network (MAW-BMRSFAN). The model is composed of BMRSFAN and MAWN. Specifically, the refined space feature attention module (RSFAM) is based on convolutional long short-term memories (ConvLSTMs). It is designed to extract refined spatial features of different dimensions for image classification and is inserted into different layers of classification model. Meanwhile, the MAWN is proposed to model the mapping from a balanced meta-dataset to imbalanced dataset. It finds suitable weighting parameter for BMRSFAN more flexibly through adaptively learning from a small amount of balanced dataset directly. The experiments show that MAW-BMRSFAN performs better than previous methods. The recognition accuracy of MAW-BMRSFAN under four different magnifications still is higher than 80% even when unbalance factor is 16, indicating that MAW-BMRSFAN can make ideal performance under extreme imbalanced conditions.

Heatmap-Guided Selective Feature Attention for Robust Cascaded Face Alignment.

Face alignment methods have been actively studied using coordinate and heatmap regression tasks. Although these regression tasks have the same objective for facial landmark detection, each task requires different valid feature maps. Therefore, it is not easy to simultaneously train two kinds of tasks with a multi-task learning network structure. Some studies have proposed multi-task learning networks with two kinds of tasks, but they do not suggest an efficient network that can train them simultaneously because of the shared noisy feature maps. In this paper, we propose a heatmap-guided selective feature attention for robust cascaded face alignment based on multi-task learning, which improves the performance of face alignment by efficiently training coordinate regression and heatmap regression. The proposed network improves the performance of face alignment by selecting valid feature maps for heatmap and coordinate regression and using the background propagation connection for tasks. This study also uses a refinement strategy that detects global landmarks through a heatmap regression task and then localizes landmarks through cascaded coordinate regression tasks. To evaluate the proposed network, we tested it on the 300W, AFLW, COFW, and WFLW datasets and obtained results that outperformed other state-of-the-art networks.

Epileptic-Net: An Improved Epileptic Seizure Detection System Using Dense Convolutional Block with Attention Network from EEG.

Epilepsy is a complex neurological condition that affects a large number of people worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and is widely used in epilepsy diagnosis, but it usually requires manual inspection, which can be hours long, by a neurologist. Several automatic systems have been proposed to detect epilepsy but still have some unsolved issues. In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure. The proposed method is largely heterogeneous and comprised of the dense convolutional blocks (DCB), feature attention modules (FAM), residual blocks (RB), and hypercolumn technique (HT). Firstly, DCB is used to get the discriminative features from the EEG samples. Then, FAM extracts the essential features from the samples. After that, RB learns more vital parts as it entirely uses information in the convolutional layer. Finally, HT retains the efficient local features extracted from the layers situated at the different levels of the model. Its performance has been evaluated on the University of Bonn EEG dataset, divided into five distinct classes. The proposed Epileptic-Net achieves the average accuracy of 99.95% in the two-class classification, 99.98% in the three-class classification, 99.96% in the four-class classification, and 99.96% in classifying the complicated five-class problem. Thus the proposed approach shows more competitive results than the existing model to detect epileptic seizures. We also hope that this method can support experts in achieving objective and reliable results, lowering the misdiagnosis rate, and assisting in decision-making.

Role of Inferior Frontal Junction (IFJ) in the Control of Feature versus Spatial Attention.

Feature-based visual attention refers to preferential selection and processing of visual stimuli based on their nonspatial attributes, such as color or shape. Recent studies have highlighted the inferior frontal junction (IFJ) as a control region for feature but not spatial attention. However, the extent to which IFJ contributes to spatial versus feature attention control remains a topic of debate. We investigated in humans of both sexes the role of IFJ in the control of feature versus spatial attention in a cued visual spatial (attend-left or attend-right) and feature (attend-red or attend-green) attention task using fMRI. Analyzing cue-related fMRI using both univariate activation and multivoxel pattern analysis, we found the following results in IFJ. First, in line with some prior studies, the univariate activations were not different between feature and spatial attentional control. Second, in contrast, the multivoxel pattern analysis decoding accuracy was above chance level for feature attention (attend-red vs attend-green) but not for spatial attention (attend-left vs attend-right). Third, while the decoding accuracy for feature attention was above chance level during attentional control in the cue-to-target interval, it was not during target processing. Fourth, the right IFJ and visual cortex (V4) were observed to be functionally connected during feature but not during spatial attention control, and this functional connectivity was positively associated with subsequent attentional selection of targets in V4, as well as with behavioral performance. These results support a model in which IFJ plays a crucial role in top-down control of visual feature but not visual spatial attention.SIGNIFICANCE STATEMENT Past work has shown that the inferior frontal junction (IFJ), a prefrontal structure, is activated by both attention-to-feature (e.g., color) and attention-to-location, but the precise role of IFJ in the control of feature- versus spatial-attention is debated. We investigated this issue in a cued visual spatial (attend-left or attend-right) and feature (attend-red or attend-green) attention task using fMRI, multivoxel pattern analysis, and functional connectivity methods. The results show that (1) attend-red versus attend-green can be decoded in single-trial cue-evoked BOLD activity in IFJ but not attend-left versus attend-right and (2) only right IFJ modulates V4 to enhance task performance. This study sheds light on the function and hemispheric specialization of IFJ in the control of visual attention.

Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images.

Accurate and robust detection of multi-class objects in very high resolution (VHR) aerial images has been playing a significant role in many real-world applications. The traditional detection methods have made remarkable progresses with horizontal bounding boxes (HBBs) due to CNNs. However, HBB detection methods still exhibit limitations including the missed detection and the redundant detection regions, especially for densely-distributed and strip-like objects. Besides, large scale variations and diverse background also bring in many challenges. Aiming to address these problems, an effective region-based object detection framework named Multi-scale Feature Integration Attention Rotation Network (MFIAR-Net) is proposed for aerial images with oriented bounding boxes (OBBs), which promotes the integration of the inherent multi-scale pyramid features to generate a discriminative feature map. Meanwhile, the double-path feature attention network supervised by the mask information of ground truth is introduced to guide the network to focus on object regions and suppress the irrelevant noise. To boost the rotation regression and classification performance, we present a robust Rotation Detection Network, which can generate efficient OBB representation. Extensive experiments and comprehensive evaluations on two publicly available datasets demonstrate the effectiveness of the proposed framework.

Neuronal Effects of Spatial and Feature Attention Differ Due to Normalization.

Although spatial and feature attention have differing effects on neuronal responses in visual cortex, it remains unclear why. Response normalization has been implicated in both types of attention (Carandini and Heeger, 2011), and single-unit studies have demonstrated that the magnitude of spatial attention effects on neuronal responses covaries with the magnitude of normalization effects. However, the relationship between feature attention and normalization remains largely unexplored. We recorded from individual neurons in the middle temporal area of rhesus monkeys using a task that allowed us to isolate the effects of feature attention, spatial attention, and normalization on the responses of each neuron. We found that the magnitudes of neuronal response modulations due to spatial attention and feature attention are correlated; however, whereas modulations due to spatial attention are correlated with normalization strength, those due to feature attention are not. Additionally, spatial attention modulations are stronger with multiple stimuli in the receptive field, whereas feature attention modulations are not. These findings are captured by a model in which spatial and feature attention share common top-down attention signals that nonetheless result in differing sensory neuron response modulations because of a spatially tuned sensory normalization mechanism. This model explains previously reported commonalities and differences between these two types of attention by clarifying the relationship between top-down attention signals and sensory normalization. We conclude that similar top-down signals to visual cortex can have distinct effects on neuronal responses due to distinct interactions with sensory mechanisms.SIGNIFICANCE STATEMENT Subjects use attention to improve their visual perception in several ways, including by attending to a location in space or to a visual feature. Prior studies have found both commonalities and differences between the effects of spatial and feature attention on neuronal responses in visual cortex, although it is unclear what mechanisms could explain this range of effects. Normalization, a computation by which neuronal responses are modified by stimulus context, has been implicated in many neuronal mechanisms throughout the brain. Here we propose that normalization provides a simple explanation for how spatial and feature attention could share common top-down attention signals that still affect sensory neuron responses differently.

Top-Down Control of Visual Attention by the Prefrontal Cortex. Functional Specialization and Long-Range Interactions.

The ability to select information that is relevant to current behavioral goals is the hallmark of voluntary attention and an essential part of our cognition. Attention tasks are a prime example to study at the neuronal level, how task related information can be selectively processed in the brain while irrelevant information is filtered out. Whereas, numerous studies have focused on elucidating the mechanisms of visual attention at the single neuron and population level in the visual cortices, considerably less work has been devoted to deciphering the distinct contribution of higher-order brain areas, which are known to be critical for the employment of attention. Among these areas, the prefrontal cortex (PFC) has long been considered a source of top-down signals that bias selection in early visual areas in favor of the attended features. Here, we review recent experimental data that support the role of PFC in attention. We examine the existing evidence for functional specialization within PFC and we discuss how long-range interactions between PFC subregions and posterior visual areas may be implemented in the brain and contribute to the attentional modulation of different measures of neural activity in visual cortices.

Attentional Modulation of Neuronal Activity Depends on Neuronal Feature Selectivity.

Attention exerts a powerful influence on visual perception. The impact of attention on neuronal activity manifests at early visual information processing stages and progressively increases throughout the visual cortical hierarchy. However, the neuronal mechanisms of attention are unresolved. In particular, the rules governing attentional modulation of individual neurons, whether they are facilitated by or suppressed by attention, are not known. To obtain a more granular or neuron- and circuit-level understanding of the mechanisms of attention and to directly test the feature similarity gain model in V1, we compared attentional modulation with neuronal feature selectivity across a large population of V1 neurons in alert and behaving macaque monkeys trained on an attention-demanding contrast-change detection task. We utilized emerging multi-electrode array technology to record simultaneously from V1 neurons spanning all six cortical layers so that we could characterize the laminar position and physiological response properties of diverse V1 neuronal populations. We found significant relationships between attentional modulation and neuronal position within the cortical hierarchy, neuronal physiology, and neuronal feature selectivity. Our results support the feature similarity gain model and further suggest that attentional modulation depends critically upon the match between neuronal feature selectivity and the features required for the task.

Feature attention for binocular disparity in primate area MT depends on tuning strength.

Attending to a stimulus modulates the responses of sensory neurons that represent features of that stimulus, a phenomenon named "feature attention." For example, attending to a stimulus containing upward motion enhances the responses of upward-preferring direction-selective neurons in the middle temporal area (MT) and suppresses the responses of downward-preferring neurons, even when the attended stimulus is outside of the spatial receptive fields of the recorded neurons (Treue S, Martinez-Trujillo JC. Nature 399: 575-579, 1999). This modulation renders the representation of sensory information across a neuronal population more selective for the features present in the attended stimulus (Martinez-Trujillo JC, Treue S. Curr Biol 14: 744-751, 2004). We hypothesized that if feature attention modulates neurons according to their tuning preferences, it should also be sensitive to their tuning strength, which is the magnitude of the difference in responses to preferred and null stimuli. We measured how the effects of feature attention on MT neurons in rhesus monkeys (Macaca mulatta) depended on the relationship between features-in our case, direction of motion and binocular disparity-of the attended stimulus and a neuron's tuning for those features. We found that, as for direction, attention to stimuli containing binocular disparity cues modulated the responses of MT neurons and that the magnitude of the modulation depended on both a neuron's tuning preferences and its tuning strength. Our results suggest that modulation by feature attention may depend not just on which features a neuron represents but also on how well the neuron represents those features.

Probability and the changing shape of response distributions for orientation.

Spatial attention and feature-based attention are regarded as two independent mechanisms for biasing the processing of sensory stimuli. Feature attention is held to be a spatially invariant mechanism that advantages a single feature per sensory dimension. In contrast to the prediction of location independence, I found that participants were able to report the orientation of a briefly presented visual grating better for targets defined by high probability conjunctions of features and locations even when orientations and locations were individually uniform. The advantage for high-probability conjunctions was accompanied by changes in the shape of the response distributions. High-probability conjunctions had error distributions that were not normally distributed but demonstrated increased kurtosis. The increase in kurtosis could be explained as a change in the variances of the component tuning functions that comprise a population mixture. By changing the mixture distribution of orientation-tuned neurons, it is possible to change the shape of the discrimination function. This prompts the suggestion that attention may not "increase" the quality of perceptual processing in an absolute sense but rather prioritizes some stimuli over others. This results in an increased number of highly accurate responses to probable targets and, simultaneously, an increase in the number of very inaccurate responses.

Direct evidence for attention-dependent influences of the frontal eye-fields on feature-responsive visual cortex.

Voluntary selective attention can prioritize different features in a visual scene. The frontal eye-fields (FEF) are one potential source of such feature-specific top-down signals, but causal evidence for influences on visual cortex (as was shown for "spatial" attention) has remained elusive. Here, we show that transcranial magnetic stimulation (TMS) applied to right FEF increased the blood oxygen level-dependent (BOLD) signals in visual areas processing "target feature" but not in "distracter feature"-processing regions. TMS-induced BOLD signals increase in motion-responsive visual cortex (MT+) when motion was attended in a display with moving dots superimposed on face stimuli, but in face-responsive fusiform area (FFA) when faces were attended to. These TMS effects on BOLD signal in both regions were negatively related to performance (on the motion task), supporting the behavioral relevance of this pathway. Our findings provide new causal evidence for the human FEF in the control of nonspatial "feature"-based attention, mediated by dynamic influences on feature-specific visual cortex that vary with the currently attended property.