Long-range Connections Research Articles

Dynamics of CH/n hydrogen bond networks probed by time-resolved CARS spectroscopy.

Hydrogen bond (HB) networks are essential for stabilizing molecular structures in solution and govern the solubility and functionality of molecules in an aqueous environment. HBs are important in biological processes such as enzyme-substrate interactions, protein folding, and DNA replication. However, the exact role of weakly polarized C-H bonds as HB proton donors in solution, such as CH/n HBs, remains mostly unknown. Here, we employ a novel approach focusing on vibrational dephasing to investigate the coherence relaxation of induced dipoles in C-H bonds within CH/n HB networks, utilizing time-resolved coherent anti-Stokes Raman scattering (T-CARS) spectroscopy. Using a representative binary system of dimethyl sulfoxide (DMSO)-water, known for its C-H backboned HB system (i.e., C-H⋯S), we observed an increase in the dephasing time of the C-H bending mode with increasing water content until a percolation threshold at a 6 : 1 water : DMSO molar ratio, where the trend is reversed. These results provide compelling evidence for the existence of C-H⋯S structures and underscore the presence of a percolation effect, suggesting a critical threshold where long-range connectivity is disputed.

DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization.

Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss. While current deep learning-based methods outperform traditional ones, they still face challenges such as the lack of local anatomical landmarks around the fovea, the inability to robustly handle diseased retinal images, and the variations in image conditions. In this paper, we propose a novel transformer-based architecture called DualStreamFoveaNet (DSFN) for multi-cue fusion. This architecture explicitly incorporates long-range connections and global features using retina and vessel distributions for robust fovea localization. We introduce a spatial attention mechanism in the dual-stream encoder to extract and fuse self-learned anatomical information, focusing more on features distributed along blood vessels and significantly reducing computational costs by decreasing token numbers. Our extensive experiments show that the proposed architecture achieves state-of-the-art performance on two public datasets and one large-scale private dataset. Furthermore, we demonstrate that the DSFN is more robust on both normal and diseased retina images and has better generalization capacity in cross-dataset experiments.

WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users Across Networks via Regularized Representation Learning

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, highly precise alignment remains challenging, especially for nodes with long-range connectivity to labeled anchors. To alleviate this limitation, we propose WL-Align which employs a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of “across-network Weisfeiler-Lehman relabeling” and “proximity-preserving representation learning”. The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the “exact matching” scenario. Data and code of WL-Align are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ChenPengGang/WLAlignCode</uri> .

Tractometry of Human Visual White Matter Pathways in Health and Disease.

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Multiple kernel clustering with local kernel reconstruction and global heat diffusion

Multiple Kernel Clustering (MKC) is an effective approach for revealing nonlinear cluster structures in candidate kernels. However, existing MKC methods still face two key challenges. Firstly, the pairwise affinity in these methods is primarily determined by kernel similarity, disregarding the correlations among highly similar neighbors and resulting in redundant weight assignments and reduced clustering discriminability. Secondly, the direct utilization of affinity matrices overlooks high-order connections and introduces noise due to independent row-wise solving. To address these issues, we propose a novel local MKC method called LKRGDF. We begin by exploring affinity using the Local Kernel Reconstruction (LKR) model, reducing redundancy and enhancing clustering discriminability. Furthermore, we exploit the affinities with the Global heat kernel Diffusion (GD) procedure to capture long-range connections smoothly. The GD process acts as a low pass filter, focusing on small eigenvalues corresponding to top clusters. Finally, we integrate these smooth affinities within an auto-weighted Multiple Graph Fusion (MGF) framework to obtain a consensus graph. By assembling LKR, GD, and MGF in a sequential pipeline, our approach achieves the exploration and exploitation of local structures, gradually improving clustering performance while ensuring computational efficiency without the need for iterative steps. Extensive experiments on ten datasets demonstrate the superiority of our algorithm in terms of effectiveness and efficiency compared to state-of-the-art methods. The code for our method is publicly available at https://github.com/YanChenSCU/LKRGDF-2023.git.

Long-range connections damage in white matter hyperintensities affects information processing speed.

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

Monosynaptic Rabies Viral Tracing Reveals Subiculum Circuit Connectivity Alterations in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly and causes progressive memory and behavioral impairment. Examining the changes in neural circuitry using AD model mice is an emerging strategy for a better understanding of AD neural mechanisms toward discovering new therapeutic targets. Our recent work indicates the disruption of long-range and local neural circuit connections in the hippocampus using an AD mouse model. The subiculum is the major output structure of the hippocampus and is among the earliest AD-impacted brain regions. We hypothesize that age-progressive alterations also occur in the neural circuit organization of the subiculum in the 5xFAD mouse model. To comprehensively map cell-type-specific circuit inputs, we utilized the novel viral-genetic tool of monosynaptic rabies tracing. We quantitatively assessed and compared the circuit connectivity of subiculum excitatory neurons in age-matched C57BL6 control and 5xFAD model mice at young and middle ages (3-4 months vs 8-9 months) in both sexes. The major subiculum input brain regions mapped by rabies tracing include hippocampal subregions, medial septum and diagonal band (MS-DB), subiculum (SUB), post subiculum (post SUB), visual (VIS) cortex, auditory (AUD) cortex, entorhinal cortex (EC), thalamus, and temporal association cortex (TeA). Our results reveal significant alterations in local and long-range circuit connections to the subiculum in AD model mice. The overall brain-wide connectivity strengths of subiculum inputs in aged AD model mice are weaker than in wild type mice. There are significant age and sex differences in the connectivity strengths of multiple input regions, including the hippocampal CA1, CA2, MS-DB, thalamus, RSC, VIS, AUD, and TeA. Our work provides new insights into subiculum- directed neural circuit mechanisms during AD progression and supports neural circuit disruptions as a prominent feature of AD.

Linear time manageable edge-aware filtering on complementary tree structures

Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from the input image. Each pixel on the MST only connects to a sub-set of pixels in the 8-connected neighborhood, resulting in piece-wise constant output with fake edges among sub-trees for the unbalanced information propagation along eight directions. In this paper, we propose two complementary spatial trees to incorporate information from the entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distances on each tree. We introduce an efficient algorithm to recursively compute the output and the normalization constant on each tree with linear time complexity. For each pixel, we first calculate the outputs from eight subtrees and then fuse them to obtain the result on each tree structure. The final filtering output of our method is the weighted average of the results from two complementary spatial trees. Moreover, we present a distance mapping scheme to adjust the intensity distance between neighboring pixels, enabling our method to filter out a manageable degree of low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifact removal, tone mapping, detail enhancement, and colorization, demonstrate the effectiveness and versatility of our method.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.

In vitro modeling of the human dopaminergic system using spatially arranged ventral midbrain–striatum–cortex assembloids

Ventral midbrain dopaminergic neurons project to the striatum as well as the cortex and are involved in movement control and reward-related cognition. In Parkinson’s disease, nigrostriatal midbrain dopaminergic neurons degenerate and cause typical Parkinson’s disease motor-related impairments, while the dysfunction of mesocorticolimbic midbrain dopaminergic neurons is implicated in addiction and neuropsychiatric disorders. Study of the development and selective neurodegeneration of the human dopaminergic system, however, has been limited due to the lack of an appropriate model and access to human material. Here, we have developed a human in vitro model that recapitulates key aspects of dopaminergic innervation of the striatum and cortex. These spatially arranged ventral midbrain–striatum–cortical organoids (MISCOs) can be used to study dopaminergic neuron maturation, innervation and function with implications for cell therapy and addiction research. We detail protocols for growing ventral midbrain, striatal and cortical organoids and describe how they fuse in a linear manner when placed in custom embedding molds. We report the formation of functional long-range dopaminergic connections to striatal and cortical tissues in MISCOs, and show that injected, ventral midbrain-patterned progenitors can mature and innervate the tissue. Using these assembloids, we examine dopaminergic circuit perturbations and show that chronic cocaine treatment causes long-lasting morphological, functional and transcriptional changes that persist upon drug withdrawal. Thus, our method opens new avenues to investigate human dopaminergic cell transplantation and circuitry reconstruction as well as the effect of drugs on the human dopaminergic system.

Plastic Spinal Motor Circuits in Health and Disease.

In the past, the spinal cord was considered a hard-wired network responsible for spinal reflexes and a conduit for long-range connections. This view has changed dramatically over the past few decades. It is now recognized as a plastic structure that has the potential to adapt to changing environments. While such changes occur under physiological conditions, the most dramatic alterations take place in response to pathological events. Many of the changes that occur following such pathological events are maladaptive, but some appear to help adapt to the new conditions. Although a number of studies have been devoted to elucidating the underlying mechanisms, in humans and animal models, the etiology and pathophysiology of various diseases impacting the spinal cord are still not well understood. In this review, we summarize current understanding and outstanding challenges for a number of diseases, including spinal muscular atrophy (SMA), amyotrophic laterals sclerosis (ALS), and spinal cord injury (SCI), with occasional relations to stroke. In particular, we focus on changes resulting from SCI (and stroke), and various influencing factors such as cause, site and extent of the afflicted damage.

Optimization of LoRa for BIoT based on ML: A case of ESL

Several technologies provide cellular connectivity to transmit data to end devices (EDs) in the indoor environment. These technologies have short-range; complex network architecture; short lifespan; and high power consumption. In this paper, LoRa is utilized to design the Building Internet of Things (BIoT) system for electronic shelf labels (ESL) to provide long-range connectivity. To minify the transmission losses, the concurrent data exploits data parallelism for updating transmission parameters at the network server (NS) for non-interruptive communication between the centroid gateways (GWs) and allocated EDs. The EDs are deployed by K-means at different locations in each cluster based on spreading factor (SF) and data rate (DR) to avoid data congestion and intra-SF interference for the SF and hybrid (DR/SF) models. The varying bandwidths (BWs) and duty cycles (DCs) enhance the lifespan of the EDs, while the Bayesian game parameter selection (BGPS) method minimizes the power losses among the EDs. The one-slope estimates medium losses; the adjusted R-square predicts variance; and Pearson finds a correlation among the measured values. The hybrid model improves the network’s performance to a spiraling average efficiency of 90.48 % and 89.64 %, with network saturation of 10.185 % and 10.337 % for pure and slotted ALOHA, respectively. The overall results illustrate a packet loss ratio (PLR) of 20.11 % for the SF model and 4.861 % for the hybrid model. The energy dissipation plunged to 0.06032 J per day, prolonging the EDs’ life span to 14265.34 days.

Feedback-related brain activity in individual decision: evidence from a gambling EEG study.

In this study, based on scalp electroencephalogram (EEG), we conducted cortical source localization and functional network analyses to investigate the underlying mechanism explaining the decision processes when individuals anticipate maximizing gambling benefits, particularly in situations where the decision outcomes are inconsistent with the profit goals. The findings shed light on the feedback monitoring process, wherein incongruity between outcomes and gambling goals triggers a more pronounced medial frontal negativity and activates the frontal lobe. Moreover, long-range theta connectivity is implicated in processing surprise and uncertainty caused by inconsistent feedback conditions, while middle-range delta coupling reflects a more intricate evaluation of feedback outcomes, which subsequently modifies individual decision-making for optimizing future rewards. Collectively, these findings deepen our comprehension of decision-making under circumstances where the profit goals are compromised by decision outcomes and provide electrophysiological evidence supporting adaptive adjustments in individual decision strategies to achieve maximum benefit.

Smart Energy Borrowing and Relaying in Wireless-Powered Networks: A Deep Reinforcement Learning Approach

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of a wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy, by using energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex, as it involves non-convex variables and requires combinatorial relay selection indicator optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimization techniques would have difficulty solving in complex problem environments. The proposed algorithm significantly improved the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%, 27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

Cortical circuit dynamics underlying motor skill learning: from rodents to humans.

Motor learning is crucial for the survival of many animals. Acquiring a new motor skill involves complex alterations in both local neural circuits in many brain regions and long-range connections between them. Such changes can be observed anatomically and functionally. The primary motor cortex (M1) integrates information from diverse brain regions and plays a pivotal role in the acquisition and refinement of new motor skills. In this review, we discuss how motor learning affects the M1 at synaptic, cellular, and circuit levels. Wherever applicable, we attempt to relate and compare findings in humans, non-human primates, and rodents. Understanding the underlying principles shared by different species will deepen our understanding of the neurobiological and computational basis of motor learning.

Controlling coherent perfect absorption via long-range connectivity of defects in three-dimensional zero-index media

Abstract Coherent perfect absorption (CPA), as time-reversed lasing, arises from appropriate wave interference within absorbers, offering flexible control over wave absorption. Typically, this control involves tuning the phase difference between two counter-propagating incident beams. Here, we elucidate the critical role of defect connectivity within three-dimensional zero-index media for realizing and controlling CPA. Specifically, the realization of CPA critically depends on the establishment of long-range connectivity of defects in a specific direction. Once the long-range connectivity is established, the CPA exhibits remarkable resilience against defects’ deformation, changes in size and shape of the zero-index media, as well as variations in number and orientation of incident channels. Notably, a minor disruption to this connectivity will result in a complete reduction of absorption to zero, highlighting an ultra-sensitive absorption property in response to connectivity perturbations. Our findings not only unveil a physical mechanism for realizing CPA but also open up promising avenues for advanced CPA control with versatile functionalities.

Contrasts in Sustainability between Hub-Based and Point-to-Point Airline Networks

Airline hubs are often defined as nodes with a high degree of connectivity. Connectivity is measured by the “degree” of the node. The degree distribution of hub networks tends to have a convex shape (curved towards the origin), while point-to-point networks have a higher number of high-degree nodes and a concave shape. This study aims to classify airline networks based on their hub orientation, expanding our understanding of network differences. The analysis in this paper involves fitting a power-law distribution, determining the range of degree distribution, and calculating the distribution of betweenness. These analyses provide insight into the classification of each airline. Each measurement helps to clarify the ambiguity in other scores. The goal is to establish a small set of rules that can clearly distinguish between the main types of networks. The classification includes four types of networks: One-hub, P2P(point-to-point), Multi-hub, and Complex networks. There is a well-recognized empirical distinction between hub networks, which have a few places with large betweenness, and point-to-point cases, which have a larger number of places with moderate betweenness. The significance of these results in terms of geographic importance is demonstrated by sorting 284 different airline networks based on these dimensions. These findings are expected to provide valuable information about the resilience and recovery of a network, as networks with many long-range connections are particularly vulnerable to a decrease in traffic. Additionally, these results have implications for the ability of networks to recover from a downturn.

Causal mediation of urban temperature by geopotential height in U.S. cities

Excessive warming is the source of many environmental challenges in urban areas. Finding causal interactions between urban ambient temperature and other meteorological variables will greatly facilitate our understanding of the underlying mechanisms of urban heat. In this study, we use the data of 2-m air temperature and 500 hPa geopotential height (GPH) in 520 cities in the contiguous United States (CONUS), from 2016 to 2022, to detect their local and nonlocal causal interactions based on the convergent cross-mapping method. For local (within the same city) interactions between temperature and GPH, there are hubs of strong causal interactions in the northern Appalachian Mountain and inland southwestern CONUS, whereas the leeward side of the southern Rocky Mountains exhibit low causation due to the baroclinic in this area. The nonlocal causal networks exhibit potential long-range connections (teleconnections), largely attributable to the influence of the upstream and downstream westerly circulations on local atmospheric variables. This study can be informative to stakeholders in design sustainable countermeasures to mitigate excessive heat in urban areas.

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients.

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.

Increased integration of functional connectivity after cognitive intervention in preschoolers from low socioeconomic status.

Cognitive interventions that involve executive functions (EF)-demanding activities are effective in changing task-related brain activity in children from homes with low socioeconomic status (SES). However, less is known about the efficiency of EF-based interventions in modifying segregation and integration properties of the functional neural organization during a resting state. Further, the consideration of the initial cognitive performance in the design of interventions and its role in the outcome of cognitive training has been poorly studied. The present study aimed to examine the impact of two individualized cognitive interventions with EF-demanding activities on brain connectivity in preschoolers (n = 79) from low-SES homes in Argentina using complex network analysis. At baseline, participants were classified as high- or low-performers based on their performance in an inhibitory control task, and then they were assigned into intervention and control groups within each performance level. Before and after the intervention, the neural activity of each child was recorded at rest using a mobile electroencephalogram device. We found significant intervention-related changes in global efficiency, global strength, and the strength of long-range connections in the θ frequency band in the intervention low-performing group. These findings support the idea that patterns of processing crucial information in the brain may be modified in children from low-SES homes through an EF-based intervention. Finally, these findings show different intervention-related effects on neural activity between children with low and high initial cognitive performance and add new evidence about the interaction between individual characteristics and intervention approaches. (PsycInfo Database Record (c) 2023 APA, all rights reserved).