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•Whole-brain “background” (stimulus-independent) functional connectivity patterns robustly differentiate between perception and memory retrieval states.•Background functional connectivity and stimulus-evoked activity patterns differ in sensitivity to different aspects of task.•Innovative functional connectivity analysis pipeline performs full correlation matrix analysis and clustering approach enabling interpretability while maintaining predictive power.•The control network shows greater within-network connectivity during the perception state, whereas the default mode network shows greater within-network connectivity during memory retrieval state.•Retrosplenial cortex switches its coupling between the control and default mode networks as the cognitive state shifts from retrieval to perception. The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in \"background\" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity (1) was fully independent from stimulus-related variance in the signal and, further, (2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

Introduction Resting‐state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large‐scale brain networks and brain–behavior relationships. Furthermore, idiosyncratic patterns of resting‐state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task‐based fMRI can be similarly leveraged for individual differences analyses. Method Here, we tested the degree to which functional interactions occurring in the background of a task during slow event‐related fMRI parallel or differ from those captured during resting‐state fMRI. We compared two approaches for removing task‐evoked activity from task‐based fMRI: (1) applying a low‐pass filter to remove task‐related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task‐evoked responses. Result We found that the organization of large‐scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task‐based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low‐pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low‐pass‐filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. Conclusion Together, our results highlight new avenues for the analysis of task‐based fMRI datasets and the utility of each background connectivity method. In this manuscript, we measured the similarity of resting‐state connectivity profiles to those obtained during slow event‐related functional magnetic resonance imaging (fMRI). Importantly, background connectivity relies on the removal of task‐evoked signals to isolate intrinsic functional interactions. Here, we compared two common methods for removing task‐evoked activity: (1) applying a low‐pass filter to the fMRI time‐series signal, or (2) extracting the residuals from a general linear model of task‐evoked responses.

The human brain has the ability to process identical information differently depending on the task. In order to perform a given task, the brain must select and react to the appropriate stimuli while ignoring other irrelevant stimuli. The dynamic nature of environmental stimuli and behavioral intentions requires an equally dynamic set of responses within the brain. Collectively, these responses act to set up and maintain states needed to perform a given task. However, the mechanisms that allow for setting up and maintaining a task state are not fully understood. Prior evidence suggests that one possible mechanism for maintaining a task state may be through altering 'background connectivity,' connectivity that exists independently of the trials of a task. Although previous studies have suggested that background connectivity contributes to a task state, these studies have typically not controlled for stimulus characteristics, or have focused primarily on relationships among areas involved with visual sensory processing. In the present study we examined background connectivity during tasks involving both visual and auditory stimuli. We examined the connectivity profiles of both visual and auditory sensory cortex that allow for selection of task-relevant stimuli, demonstrating the existence of a potentially universal pattern of background connectivity underlying attention to a stimulus. Participants were presented with simultaneous auditory and visual stimuli and were instructed to respond to only one, while ignoring the other. Using functional MRI, we observed task-based modulation of the background connectivity profile for both the auditory and visual cortex to certain brain regions. There was an increase in background connectivity between the task-relevant sensory cortex and control areas in the frontal cortex. This increase in synchrony when receiving the task-relevant stimulus as compared to the task irrelevant stimulus may be maintaining paths for passing information within the cortex. These task-based modulations of connectivity occur independently of stimuli and could be one way the brain sets up and maintains a task state. •Background connectivity represents shift in the dynamics of ongoing neural activity.•Background connectivity changes with task.•Set of right frontal regions increase connection to task-relevant sensory cortex.•Enhanced connectivity to these 4 frontal regions may reflect control of sensory inputs.•Background connectivity may be one mechanism for maintaining a task state.

Attention facilitates the processing of task-relevant visual information and suppresses interference from task-irrelevant information. Modulations of neural activity in visual cortex depend on attention, and likely result from signals originating in fronto-parietal and cingulo-opercular regions of cortex. Here, we tested the hypothesis that attentional facilitation of visual processing is accomplished in part by changes in how brain networks involved in attentional control interact with sectors of V1 that represent different retinal eccentricities. We measured the strength of background connectivity between fronto-parietal and cingulo-opercular regions with different eccentricity sectors in V1 using functional MRI data that were collected while participants performed tasks involving attention to either a centrally presented visual stimulus or a simultaneously presented auditory stimulus. We found that when the visual stimulus was attended, background connectivity between V1 and the left frontal eye fields (FEF), left intraparietal sulcus (IPS), and right IPS varied strongly across different eccentricity sectors in V1 so that foveal sectors were more strongly connected than peripheral sectors. This retinotopic gradient was weaker when the visual stimulus was ignored, indicating that it was driven by attentional effects. Greater task-driven differences between foveal and peripheral sectors in background connectivity to these regions were associated with better performance on the visual task and faster response times on correct trials. These findings are consistent with the notion that attention drives the configuration of task-specific functional pathways that enable the prioritized processing of task-relevant visual information, and show that the prioritization of visual information by attentional processes may be encoded in the retinotopic gradient of connectivty between V1 and fronto-parietal regions.

Boundary and connectivity prior are common methods for detecting the image salient object. They often address two problems: 1) if the salient object touches the image boundary, the saliency of the object will fail, and 2) accurate pixel-wise or superpixel-wise computation needs high time expenditure. This study proposes a block-wise algorithm to reduce calculation time expenditure and suppress the salient objects touching the image boundary. The algorithm consists of four stages. In the first stage, each block is analyzed by an adaptive micro and macro prediction technique to generate a saliency prediction map. The second stage selects background and salient sources from the saliency prediction map. Background sources are extracted from the image boundary with low saliency value. Salient sources are accurately positioned in the region of salient objects. In the third stage, the background and salient sources are used to generate the background path and salient path based on minimum barrier distance. The block-wise initial saliency map is obtained by fusing the background and salient paths. In the fourth stage, major-color modeling technology and visual focus priors are used to complete the refinement of the saliency map to improve the block effect. In the experimental result, the proposed method produced the best test results among other algorithms in three dataset tests and achieved 284 frames per second (FPS) speed performance on the MSRA-10 K dataset. Our method shows at least 29.09% speed improvement and executes in real-time on a lightweight embedded platform.

Smooth pursuit eye movements (SPEM) are essential to guide behaviour in complex visual environments. SPEM accuracy is known to be degraded by the presence of a structured visual background and at higher target velocities. The aim of this preregistered study was to investigate the neural mechanisms of these robust behavioural effects. N = 33 participants performed a SPEM task with two background conditions (present and absent) at two target velocities (0.4 and 0.6 Hz). Eye movement and BOLD data were collected simultaneously. Both the presence of a structured background and faster target velocity decreased pursuit gain and increased catch‐up saccade rate. Faster targets additionally increased position error. Higher BOLD response with background was found in extensive clusters in visual, parietal, and frontal areas (including the medial frontal eye fields; FEF) partially overlapping with the known SPEM network. Faster targets were associated with higher BOLD response in visual cortex and left lateral FEF. Task‐based functional connectivity analyses (psychophysiological interactions; PPI) largely replicated previous results in the basic SPEM network but did not yield additional information regarding the neural underpinnings of the background and velocity effects. The results show that the presentation of visual background stimuli during SPEM induces activity in a widespread visuo‐parieto‐frontal network including areas contributing to cognitive aspects of oculomotor control such as medial FEF, whereas the response to higher target velocity involves visual and motor areas such as lateral FEF. Therefore, we were able to propose for the first time different functions of the medial and lateral FEF during SPEM. Visual background stimuli reduce smooth pursuit accuracy and are associated with enhanced brain activity in a widespread occipito‐parieto‐frontal brain network. Faster targets similarly reduce smooth pursuit performance but yield enhanced BOLD response only in visual cortex and lateral frontal eye fields. Functional connectivity analyses confirmed the interactions amongst components of the SPEM network but did not yield differences between task conditions.

Synthetic aperture radar (SAR) is an active microwave imaging system equipped with penetration capability, enabling all-time and all-weather Earth observation, and demonstrates significant advantages in large-scale surface water-body detection. Although SAR images can provide relatively clear water-body details, they are susceptible to interference from external factors such as complex terrain and background noise, resulting in fragmented detection outcomes and poor connectivity. Therefore, a Connectivity Refinement Network (ConRNet) is proposed in this study to address the issue of fragmented water-body regions in water-body detection results, combining HISEA-1 and Chaohu-1 SAR data. ConRNet is equipped with attention mechanisms and a connectivity prediction module, combined with dual supervision from segmentation and connectivity labels. Unlike conventional attention modules that only emphasize pixel-wise saliency, the proposed Dual Self-Attention Module (DSAM) jointly captures spatial and channel dependencies. Meanwhile, the Connectivity Prediction Module (CPM) reformulates water-body connectivity as a regression problem to directly optimize structural coherence without relying on post-processing. Leveraging dual supervision from segmentation and connectivity labels, ConRNet achieves simultaneous improvements in topological consistency and pixel-level accuracy. The performance of the proposed ConRNet is evaluated by con-ducting comparative experiments with five deep learning models: FCN, U-Net, DeepLabv3+, HRNet, and MAGNet. The experimental results demonstrate that the ConRNet achieves the highest accuracy in water-body detection, with an intersection over union (IoU) of 88.59% and an F1-score of 93.87%.

Binarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

What are the main findings? * GeoRoad-UPerNet achieves the strongest overall road-extraction performance under Geo-1-only, Sentinel-2-only, and fusion-based settings. * The largest gain is observed in the fusion-based benchmark, with cleaner extraction boundaries and more stable preservation of road structures. GeoRoad-UPerNet achieves the strongest overall road-extraction performance under Geo-1-only, Sentinel-2-only, and fusion-based settings. The largest gain is observed in the fusion-based benchmark, with cleaner extraction boundaries and more stable preservation of road structures. What are the implications of the main findings? * Role-aware organization of multi-source observations improves the accuracy and reliability of road extraction under proxy supervision. * The study provides a practical framework for road extraction when dense manual labels are unavailable but map-derived supervision can still be constructed. Role-aware organization of multi-source observations improves the accuracy and reliability of road extraction under proxy supervision. The study provides a practical framework for road extraction when dense manual labels are unavailable but map-derived supervision can still be constructed. Extracting roads accurately from remote sensing images is important for map updates, traffic analysis, and infrastructure monitoring. Medium-resolution multispectral images can provide useful surface and background information, but when used alone, the spatial details are limited for retaining narrow roads, intersection structures, and fine road topologies. To address this problem, this paper proposes GeoRoad-UPerNet, a Geo-1-centered weakly supervised multispectral framework for road extraction. In this framework, Geo-1 serves as the primary 16-band multispectral source, Sentinel-2 Level-2A imagery serves as auxiliary contextual support, and OpenStreetMap (OSM) road information is converted into proxy supervision rather than dense manual ground truth. GeoRoad-UPerNet contains three modules: a Geo Spectral Semantic Stem (GSSS), a Geo-Auxiliary Gated Fusion module (GAGF), and a Road Semantic Multi-Task Head (RSMH). GSSS strengthens road-sensitive multispectral responses in the Geo-1 branch. GAGF injects Sentinel-2 context through a Geo-centered gate instead of symmetric channel concatenation. RSMH imposes restrained hierarchy- and material-aware semantic regularization on the shared decoder representation during training. On the fixed source-domain benchmark, the complete model achieves an IoU of 0.7204, an F1-score of 0.8375, a Precision of 0.8092, and a Recall of 0.8678 against OSM-derived proxy masks. Relative to the UPerNet-MiT-B3 early-fusion baseline, IoU, F1-score, and Precision increase by 6.29%, 3.65%, and 12.58%, respectively. These results indicate that role-aware multisource organization improves road extraction under proxy supervision and reduces boundary noise and background false positives.

•EEG functional networks after perinatal stroke are more clustered, less modular and have high interhemispheric strength.•Children after perinatal stroke have lower intelligence, poorer visual-motor integration abilities and attention deficits.•Modularity of functional brain networks correlates positively with IQ and processing speed.•Higher network segregation and stronger functional connectivity correlate with a tendency to impulsive decision making. Mechanisms underlying cognitive impairment after perinatal stroke could be explained through brain network alterations. With aim to explore this connection, we conducted a matched test-control study to find a correlation between functional brain network properties and cognitive functions in children after perinatal stroke. First, we analyzed resting-state functional connectomes in the alpha frequency band from a 64-channel resting state EEG in 24 children with a history of perinatal stroke (12 with neonatal arterial ischemic stroke and 12 with neonatal hemorrhagic stroke) and compared them to the functional connectomes of 24 healthy controls. Next, all participants underwent cognitive evaluation. We analyzed the differences in functional brain network properties and cognitive abilities between groups and studied the correlation between network characteristics and specific cognitive functions. Functional brain networks after perinatal stroke had lower modularity, higher clustering coefficient, higher interhemispheric strength, higher characteristic path length and higher small world index. Modularity correlated positively with the IQ and processing speed, while clustering coefficient correlated negatively with IQ. Graph metrics, reflecting network segregation (clustering coefficient and small world index) correlated positively with a tendency to impulsive decision making, which also correlated positively with graph metrics, reflecting stronger functional connectivity (characteristic path length and interhemispheric strength). Our study suggests that specific cognitive functions correlate with different brain network properties and that functional network characteristics after perinatal stroke reflect poorer cognitive functioning.