Deep Networks for Image Super-Resolution with Sparse Prior

Deep learning techniques have been successfully applied in many areas of computer vision, including low-level image restoration problems. For image super-resolution, several models based on deep neural networks have been recently proposed and attained superior performance that overshadows all previous handcrafted models. The question then arises whether large-capacity and data-driven models have become the dominant solution to the ill-posed super-resolution problem. In this paper, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end. The interpretation of the network based on sparse coding leads to much more efficient and effective training, as well as a reduced model size. Our model is evaluated on a wide range of images, and shows clear advantage over existing state-of-the-art methods in terms of both restoration accuracy and human subjective quality

Unlocking system transitions for municipal solid waste infrastructure:A model for mapping interdependencies in a local context

Rapid global urbanization, urban renewal and changes in people's lifestyles have led to both an increase in waste generation and more complex waste types. In response to these changes, many local governments have invested in municipal solid waste infrastructure (MSWI) to implement circular strategies. However, matching and bridging the costly and logistically complex MSWI with the dynamic social context is a central challenge. In this paper we aim to explore the interdependencies between MSWI and the local social system, and then conceptualize and empirically validate the systemic nature of MSWI. We first review the current MSW treatment methods, corresponding infrastructure, and the challenges facing them. Then, we interrogate system-oriented concepts and use two key insights to set up a conceptual model for mapping the interdependencies in a MSWI system (MSWIS). Finally, a case study of the Dutch city of Almere is used to empirically validate the MSWIS model and identify the social systems that contribute to the development of the MSWIS. The analysis reveals that the development of MSWIS is beyond the municipality's control: efficient resource recovery facilities established by businesses under market rules and waste reuse facilities constructed by social organizations/individuals based on their own needs are key pieces of the puzzle to complete the MSWIS. This highlights the ability of the framework to capture interdependencies that go further than just the formal municipal sphere of influence.</p

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation

The genetic determinants of language network dysconnectivity in drug-naïve early stage schizophrenia

Schizophrenia is a neurocognitive illness of synaptic and brain network-level dysconnectivity that often reaches a persistent chronic stage in many patients. Subtle language deficits are a core feature even in the early stages of schizophrenia. However, the primacy of language network dysconnectivity and language-related genetic variants in the observed phenotype in early stages of illness remains unclear. This study used two independent schizophrenia dataset consisting of 138 and 53 drug-naïve first-episode schizophrenia (FES) patients, and 112 and 56 healthy controls, respectively. A brain-wide voxel-level functional connectivity analysis was conducted to investigate functional dysconnectivity and its relationship with illness duration. We also explored the association between critical language-related genetic (such as FOXP2) mutations and the altered functional connectivity in patients. We found elevated functional connectivity involving Broca’s area, thalamus and temporal cortex that were replicated in two FES datasets. In particular, Broca’s area - anterior cingulate cortex dysconnectivity was more pronounced for patients with shorter illness duration, while thalamic dysconnectivity was predominant in those with longer illness duration. Polygenic risk scores obtained from FOXP2-related genes were strongly associated with functional dysconnectivity identified in patients with shorter illness duration. Our results highlight the criticality of language network dysconnectivity, involving the Broca’s area in early stages of schizophrenia, and the role of language-related genes in this aberration, providing both imaging and genetic evidence for the association between schizophrenia and the determinants of language

Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals.

Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments

Reward vs non-reward sensitivity of the medial vs lateral orbitofrontal cortex relates to the severity of depressive symptoms

Background: The orbitofrontal cortex (OFC) is implicated in depression. The hypothesis investigated was whether the OFC sensitivity to reward and non-reward is related to the severity of depressive symptoms. Methods: Activations in the monetary incentive delay task were measured in the IMAGEN cohort at age 14 (n=1877) and 19 (n=1140) with a longitudinal design. Clinically-relevant subgroups were compared at age 19 (high-severity group n=116; low-severity group n=206), and 14. Results: The medial OFC exhibited graded activation increases to reward; and the lateral OFC had graded activation increases to non-reward. In this general population, the medial and lateral OFC activations were associated with concurrent depressive symptoms at both age 14 and 19. In a stratified high-severity depressive symptom vs control comparison, the lateral OFC showed greater sensitivity for the magnitudes of activations related to non-reward (No-Win) in the high-severity group at age 19 (p=0.027), and the medial OFC showed decreased sensitivity to the reward magnitudes in the high-severity group at both age 14 (p=0.002) and 19 (p=0.002). In a longitudinal design, there was greater sensitivity to non-reward of the lateral OFC at age 14 for those who exhibited high depressive symptom severity later at age 19 (p=0.003). Conclusions: Activations in the lateral orbitofrontal cortex relate to sensitivity to not winning, were associated with high depressive symptom scores, and at 14 predicted the depressive symptoms at 16 and 19. Activations in the medial OFC were related to sensitivity to winning, and reduced reward sensitivity was associated with concurrent high depressive symptom scores