Higher-order Processes Research Articles

Short communication: Lifetime musical activity and resting-state functional connectivity in cognitive networks.

Participation in multimodal leisure activities, such as playing a musical instrument, may be protective against brain aging and dementia in older adults (OA). Potential neuroprotective correlates underlying musical activity remain unclear. This cross-sectional study investigated the association between lifetime musical activity and resting-state functional connectivity (RSFC) in three higher-order brain networks: the Default Mode, Fronto-Parietal, and Salience networks. We assessed 130 cognitively unimpaired participants (≥ 60 years) from the baseline cohort of the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Lifetime musical activity was operationalized by the self-reported participation in musical instrument playing across early, middle, and late life stages using the Lifetime of Experiences Questionnaire (LEQ). Participants who reported musical activity during all life stages (n = 65) were compared to controls who were matched on demographic and reserve characteristics (including education, intelligence, socioeconomic status, self-reported physical activity, age, and sex) and never played a musical instrument (n = 65) in local (seed-to-voxel) and global (within-network and between-network) RSFC patterns using pre-specified network seeds. Older participants with lifetime musical activity showed significantly higher local RSFC between the medial prefrontal cortex (Default Mode Network seed) and temporal as well as frontal regions, namely the right temporal pole and the right precentral gyrus extending into the superior frontal gyrus, compared to matched controls. There were no significant group differences in global RSFC within or between the three networks. We show that playing a musical instrument during life relates to higher RSFC of the medial prefrontal cortex with distant brain regions involved in higher-order cognitive and motor processes. Preserved or enhanced functional connectivity could potentially contribute to better brain health and resilience in OA with a history in musical activity. German Clinical Trials Register (DRKS00007966, 04/05/2015).

Social interactions and information use by foraging seabirds.

What do seabirds perceive about the world? How do they do so? And how do they use the information available to them to make foraging decisions? Social cues provide seabirds with information about the location of prey. This can, of course, be passive and not involve higher-order cognitive processes (e.g. simple conspecific or heterospecific attraction). However, seabirds display many behaviours that promote learning and the transmission of information between individuals: the vast majority of seabirds are colonial living, have an extended juvenile phase that affords them time to learn, routinely form intra- and interspecific associations, and can flexibly deploy a combination of foraging tactics. It is worth evaluating their foraging interactions in light of this. This review describes how seabirds use social information both at the colony and at sea to forage, and discusses the variation that exists both across species and amongst individuals. It is clear that social interactions are a critical and beneficial component of seabird foraging, with most of the variation concerning the way and extent to which social information is used, rather than whether it is used. While it may seem counterintuitive that large groups of potential competitors congregating at a patch can result in foraging gains, such aggregations can alter species dynamics in ways that promote coexistence. This review explores how competitive interference at a patch can be mitigated by behavioural modifications and niche segregation. Utilising others for foraging success (e.g. via social cues and facilitation at a patch) is likely to make population declines particularly damaging to seabirds if the quantity or quality of their social foraging interactions is reduced. Environmental changes have the potential to disrupt their social networks and thus, how these species obtain food and transfer information.

Adaptive responding to stimulus-outcome associations requires noradrenergic transmission in the medial prefrontal cortex.

A dynamic environment, such as the one we inhabit, requires organisms to continuously update their knowledge of the setting. While the prefrontal cortex is recognized for its pivotal role in regulating such adaptive behavior, the specific contributions of each prefrontal area remain elusive. In the current work, we investigated the direct involvement of two major prefrontal subregions, the medial prefrontal cortex (mPFC, A32D+A32V) and the orbitofrontal cortex (OFC, VO+LO), in updating Pavlovian stimulus-outcome (S-O) associations following contingency degradation in male rats. Specifically, animals had to learn that a particular cue, previously fully predicting the delivery of a specific reward, was no longer a reliable predictor. First, we found that chemogenetic inhibition of mPFC, but not of OFC, neurons altered the rats' ability to adaptively respond to degraded and non-degraded cues. Next, given the growing evidence pointing at noradrenaline (NA) as a main neuromodulator of adaptive behavior, we decided to investigate the possible involvement of NA projections to the two subregions in this higher-order cognitive process. Employing a pair of novel retrograde vectors, we traced NA projections from the locus coeruleus (LC) to both structures and observed an equivalent yet relatively segregated amount of inputs. Then, we showed that chemogenetic inhibition of NA projections to the mPFC, but not to the OFC, also impaired the rats' ability to adaptively respond to the degradation procedure. Altogether, our findings provide important evidence of functional parcellation within the prefrontal cortex and point at mPFC-NA as key for updating Pavlovian S-O associations.Significant statement The ability to update stimulus-outcome (S-O) associations is a key adaptive behavior, essential for surviving and thriving in an ever-changing environment. The prefrontal cortex is well-known for playing a key role in this process. The discrete contribution of each prefrontal subregion and of different neurotransmitters, however, remains unclear. In the current study, we show that inhibiting medial prefrontal (mPFC), but not orbitofrontal cortex (OFC), neurons impairs rats' ability to update S-O associations following contingency degradation. Moreover, we demonstrate that discrete noradrenergic projections to the two subregions exist and that inhibiting the ones projecting to the mPFC, but not to the OFC, once again impairs the animals' behavior, thereby implying a substantial contribution of noradrenaline in orchestrating this higher-order cognitive process.

LCL-T Module Reduction of Offshore Photovoltaic Power Generation System Based on Current Feedback-routh Approximation Method

The rapid development of offshore photovoltaic power generation has attracted more and more attention. Aiming at the problem of poor output power quality caused by the randomness and intermittentness of offshore photovoltaic power generation, LCL filters and isolation transformers are widely used on the AC side of photovoltaic power generation systems to improve the power quality of Grid-connected. Although this method has a good suppression effect on harmonics and DC components, its higher order will result in a larger amount of calculation, especially in the simulation of large-scale photovoltaic grid-connected systems, the operating speed will be too slow. In order to increase the simulation rate while ensuring the quality of the output power, the high order LCL-T (LCL filter and isolation transformer) module of the photovoltaic power generation system is reduced in order. When the transformer is not saturated, the LCL-T module reduction method based on the weighted current feedback method is adopted, and the purpose of simplification is achieved through the zero-pole cancellation of the transfer function; When the transformer is saturated, the LCL-T module reduction method based on the Routh approximation method is adopted, Use advanced mathematics formulas to reduce the order of high-order system equivalent processing. The PSCAD/EMTDC simulation software verifies the off-grid characteristics and simulation rate of the reduced-order model.

Functional mapping of the somatosensory cortex using noninvasive fMRI and touch in awake dogs.

Dogs are increasingly used as a model for neuroscience due to their ability to undergo functional MRI fully awake and unrestrained, after extensive behavioral training. Still, we know rather little about dogs' basic functional neuroanatomy, including how basic perceptual and motor functions are localized in their brains. This is a major shortcoming in interpreting activations obtained in dog fMRI. The aim of this preregistered study was to localize areas associated with somatosensory processing. To this end, we touched N = 22 dogs undergoing fMRI scanning on their left and right flanks using a wooden rod. We identified activation in anatomically defined primary and secondary somatosensory areas (SI and SII), lateralized to the contralateral hemisphere depending on the side of touch, and importantly also activation beyond SI and SII, in the cingulate cortex, right cerebellum and vermis, and the sylvian gyri. These activations may partly relate to motor control (cerebellum, cingulate), but also potentially to higher-order cognitive processing of somatosensory stimuli (rostral sylvian gyri), and the affective aspects of the stimulation (cingulate). We also found evidence for individual side biases in a vast majority of dogs in our sample, pointing at functional lateralization of somatosensory processing. These findings not only provide further evidence that fMRI is suited to localize neuro-cognitive processing in dogs, but also expand our understanding of in vivo touch processing in mammals, beyond classically defined primary and secondary somatosensory cortices.

Differences in scanpath pattern and verbal working memory predicts efficient reading in the Cloze gap-filling test.

Different tests measure text comprehension, including the cloze gap-filling test, often used for language learning. Different studies hypothesized cognitive strategies in this type of test and their relationship with working memory and performance. However, no study investigated the cloze test, working memory, and possible cognitive strategies, while performing the test. Therefore, this study aimed to identify cognitive visual strategies in the cloze test by applying an unsupervised algorithm and to analyze the relationship between these strategies with working memory and performance in the cloze test. Our sample consisted of 51 university students, the largest sample in studies of cognitive strategies with cloze tests. Participants answered an 11-item cloze test in a computer with eye-tracking, a verbal working memory test, and a visuospatial working memory test. Our analysis of participants' scanpath identified two main strategies: one with fewer toggles between text and word bank and fewer fixations than the other one, indicating the existence of a global strategy. Furthermore, a model predicting the efficiency of participants in the cloze test found that item complexity, using a global strategy, and higher scores of working memory were the most significant predictors. These results confirm the hypothesis of a global strategy being related to successfully achieving higher-order reading processes.

Bayesian Inference of Recurrent Switching Linear Dynamical Systems with Higher-Order Dependence

Many complicated dynamical events may be broken down into simpler pieces and efficiently described by a system that shifts among a variety of conditionally dynamical modes. Building on switching linear dynamical systems, we develop a new model that extends the switching linear dynamical systems for better discovering these dynamical modes. In the proposed model, the linear dynamics of latent variables can be described by a higher-order vector autoregressive process, which makes it feasible to evaluate the higher-order dependency relationships in the dynamics. In addition, the transition of switching states is determined by a stick-breaking logistic regression, overcoming the limitation of a restricted geometric state duration and recovering the symmetric dependency between the switching states and the latent variables from asymmetric relationships. Furthermore, logistic regression evidence potentials can appear as conditionally Gaussian potentials by utilizing the Po´lya-gamma augmentation strategy. Filtering and smoothing algorithms and Bayesian inference for parameter learning in the proposed model are presented. The utility and versatility of the proposed model are demonstrated on synthetic data and public functional magnetic resonance imaging data. Our model improves the current methods for learning the switching linear dynamical modes, which will facilitate the identification and assessment of the dynamics of complex systems.

Effect of spectral degradation on speech intelligibility and cortical representation.

Noise-vocoded speech has long been used to investigate how acoustic cues affect speech understanding. Studies indicate that reducing the number of spectral channel bands diminishes speech intelligibility. Despite previous studies examining the channel band effect using earlier event-related potential (ERP) components, such as P1, N1, and P2, a clear consensus or understanding remains elusive. Given our hypothesis that spectral degradation affects higher-order processing of speech understanding beyond mere perception, we aimed to objectively measure differences in higher-order abilities to discriminate or interpret meaning. Using an oddball paradigm with speech stimuli, we examined how neural signals correlate with the evaluation of speech stimuli based on the number of channel bands measuring N2 and P3b components. In 20 young participants with normal hearing, we measured speech intelligibility and N2 and P3b responses using a one-syllable task paradigm with animal and non-animal stimuli across four vocoder conditions with 4, 8, 16, or 32 channel bands. Behavioral data from word repetition clearly affected the number of channel bands, and all pairs were significantly different (p < 0.001). We also observed significant effects of the number of channels on the peak amplitude [F(2.006, 38.117) = 9.077, p < 0.001] and peak latency [F(3, 57) = 26.642, p < 0.001] of the N2 component. Similarly, the P3b component showed significant main effects of the number of channel bands on the peak amplitude [F(2.231, 42.391) = 13.045, p < 0.001] and peak latency [F(3, 57) = 2.968, p = 0.039]. In summary, our findings provide compelling evidence that spectral channel bands profoundly influence cortical speech processing, as reflected in the N2 and P3b components, a higher-order cognitive process. We conclude that spectrally degraded one-syllable speech primarily affects cortical responses during semantic integration.

The entropy rate of Linear Additive Markov Processes.

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy.

A hierarchy of visual processing deficits in body dysmorphic disorder: a conceptual review and empirical investigation

ABSTRACT Objective Abnormal visual processing has been proposed as a mechanism underlying excessive focus on minor appearance flaws in body dysmorphic disorder (BDD). Existing BDD research has not differentiated the various stages of face processing (featural, first-order configural, holistic and second-order configural) that are required for higher-order processes such as emotion recognition. This study investigated a hierarchical visual processing model to examine the nature of abnormalities in face processing in BDD. Method Thirty BDD participants and 27 healthy controls completed the Navon task, a featural and configural face processing task and a facial emotion labelling task. Results BDD participants performed similarly to controls when processing global and local non-face stimuli on the Navon task, when detecting subtle changes in the features and spacing of a target face, and when labelling emotional faces. However, BDD participants displayed poorer performance when viewing inverted faces, indicating difficulties in configural processing. Conclusions The findings only partially support prior work. However, synthesis of results with previous findings indicates that heterogenous task methodologies may contribute to inconsistent findings. Recommendations are provided regarding the task parameters that appear most sensitive to abnormalities in BDD.

Mental workload modulates the effects of baroreceptor afferents on sensorimotor processing

The heart&amp;ndash;brain interaction is the main mechanism for maintaining normative physiological processes, and its dysregulation underlies the somatic symptoms of various mental disorders. Cortical inhibition, triggered by afferent signals from baroreceptor activation, induces systematic variations in sensorimotor responses within a cardiac cycle, with reaction times (RTs) slower at cardiac systole compared to diastole (known as cardiac cycle time effects). However, recent data suggest that baroreceptor afferents not only inhibit simple responses but also facilitate complex sensorimotor responses during cardiac systole. The mental workload that is implicated in complex responses may modulate the cardiac cycle time effects. The current study aimed to examine whether concurrent mental workloads influenced cardiac cycle time effects on sensorimotor processing. Using a dual-task paradigm, 47 participants (32 female; age = 21.9 &amp;plusmn; 2.1 years) performed a choice RT task and a working memory (WM) task. Stimuli were presented during either cardiac systole or diastole. RT data were fitted using the ex-Gaussian distribution, and the parameters, mu and tau, were derived to indicate response speed and high-order attentional processes, respectively. The behavioral data were submitted to 2 (WM load) &amp;times; 2 (cardiac timing) repeated measures analyses of variances. The results indicated that RT metrics were longer during cardiac systole than diastole under the no-load condition. However, WM load reversed the cardiac timing effects on response speed while inducing more attentional lapses. These findings suggest that concurrent WM load influences cardiac cycle time effects on sensorimotor processing via top-down resources.

Spaced training activates Miro/Milton-dependent mitochondrial dynamics in neuronal axons to sustain long-term memory

Neurons have differential and fluctuating energy needs across distinct cellular compartments, shaped by brain electrochemical activity associated with cognition. Invitro studies show that mitochondria transport from soma to axons is key to maintaining neuronal energy homeostasis. Nevertheless, whether the spatial distribution of neuronal mitochondria is dynamically adjusted invivo in an experience-dependent manner remains unknown. In Drosophila, associative long-term memory (LTM) formation is initiated by an early and persistent upregulation of mitochondrial pyruvate flux in the axonal compartment of neurons in the mushroom body (MB). Through behavior experiments, super-resolution analysis of mitochondria morphology in the neuronal soma and invivo mitochondrial fluorescence recovery after photobleaching (FRAP) measurements in the axons, we show that LTM induction, contrary to shorter-lived memories, is sustained by the departure of some mitochondria from MB neuronal soma and increased mitochondrial dynamics in the axonal compartment. Accordingly, impairing mitochondrial dynamics abolished the increased pyruvate consumption, specifically after spaced training and in the MB axonal compartment, thereby preventing LTM formation. Our results thus promote reorganization of the mitochondrial network in neurons as an integral step in elaborating high-order cognitive processes.

Entanglement in Resonance Fluorescence

Particle entanglement is a fundamental resource upon which are based many quantum technologies. In this Article, we introduce a new source of entangled photons based on Resonance Fluorescence delivering photon pairs as a superposition of vacuum and the Bell state Φ−\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left\\vert {{{\\Phi }}}^{-}\\right\\rangle$$\\end{document}. Our proposal relies on the emission from the satellite peaks of a two-level system driven by a strong off-resonant laser, whose intensity controls the frequencies of the entangled photons. Notably, such a frequency tuning can be done without decreasing the degree of entanglement between the photons and, unlike current technologies, the intensity of our source can be increased without the risk of spoiling the signal by including higher-order processes into the emission. Finally, we illustrate the power of our novel source by exciting an ubiquitous condensed-matter system, namely exciton-polaritons, and show that they are left in a maximally entangled steady state.

Evaluating Speaker-Listener Cognitive Effort in Speech Communication Through Brain-to-Brain Synchrony: A Pilot Functional Near-Infrared Spectroscopy Investigation.

We explore a new approach to the study of cognitive effort involved in listening to speech by measuring the brain activity in a listener in relation to the brain activity in a speaker. We hypothesize that the strength of this brain-to-brain synchrony (coupling) reflects the magnitude of cognitive effort involved in verbal communication and includes both listening effort and speaking effort. We investigate whether interbrain synchrony is greater in native-to-native versus native-to-nonnative communication using functional near-infrared spectroscopy (fNIRS). Two speakers participated, a native speaker of American English and a native speaker of Korean who spoke English as a second language. Each speaker was fitted with the fNIRS cap and told short stories. The native English speaker provided the English narratives, and the Korean speaker provided both the nonnative (accented) English and Korean narratives. In separate sessions, fNIRS data were obtained from seven English monolingual participants ages 20-24 years who listened to each speaker's stories. After listening to each story in native and nonnative English, they retold the content, and their transcripts and audio recordings were analyzed for comprehension and discourse fluency, measured in the number of hesitations and articulation rate. No story retellings were obtained for narratives in Korean (an incomprehensible language for English listeners). Utilizing fNIRS technique termed sequential scanning, we quantified the brain-to-brain synchronization in each speaker-listener dyad. For native-to-native dyads, multiple brain regions associated with various linguistic and executive functions were activated. There was a weaker coupling for native-to-nonnative dyads, and only the brain regions associated with higher order cognitive processes and functions were synchronized. All listeners understood the content of all stories, but they hesitated significantly more when retelling stories told in accented English. The nonnative speaker hesitated significantly more often than the native speaker and had a significantly slower articulation rate. There was no brain-to-brain coupling during listening to Korean, indicating a break in communication when listeners failed to comprehend the speaker. We found that effortful speech processing decreased interbrain synchrony and delayed comprehension processes. The obtained brain-based and behavioral patterns are consistent with our proposal that cognitive effort in verbal communication pertains to both the listener and the speaker and that brain-to-brain synchrony can be an indicator of differences in their cumulative communicative effort. https://doi.org/10.23641/asha.25452142.

Harnessing the Power of AI: A Comprehensive Review of Left Ventricular Ejection Fraction Assessment With Echocardiography.

The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.

Effective multiband synthetic four-wave mixing by cascading quadratic processes

Four-wave mixing (FWM) is an important technique for supercontinuum and frequency comb generation in the mid-infrared band. Here, we report simultaneous synthetic FWM in both the visible and mid-infrared bands by cascading quadratic nonlinear processes in a periodically poled lithium niobate (PPLN) crystal, which has a conversion efficiency that is 110 dB (at 3000 nm) higher than the FWM generated directly using third-order susceptibilities in bulk PPLN crystals. A general model of the proposed process is developed that shows full agreement with the experimental verification results. The frequency difference between the emerging frequency components can be tuned freely by varying the frequency difference between the dual pump lasers. Furthermore, by increasing the conversion bandwidth and the efficiency of the cascaded processes, it becomes feasible to generate frequency combs simultaneously in three bands, comprising the visible, near-infrared, and mid-infrared bands, via high-order cascaded processes. This work represents a route toward free-tuning multiband frequency comb generation with multi-octave frequency spanning that will have significant applications in fields, including mid-infrared gas sensing, lidar, and high-precision spectroscopy.

Higher-order connection Laplacians for directed simplicial complexes

Higher-order networks encode the many-body interactions existing in complex systems, such as the brain, protein complexes, and social interactions. Simplicial complexes are higher-order networks that allow a comprehensive investigation of the interplay between topology and dynamics. However, simplicial complexes have the limitation that they only capture undirected higher-order interactions while in real-world scenarios, often there is a need to introduce the direction of simplices, extending the popular notion of direction of edges. On graphs and networks the Magnetic Laplacian, a special case of connection Laplacian, is becoming a popular operator to address edge directionality. Here we tackle the challenge of handling directionality in simplicial complexes by formulating higher-order connection Laplacians taking into account the configurations induced by the simplices’ directions. Specifically, we define all the connection Laplacians of directed simplicial complexes of dimension two and we discuss the induced higher-order diffusion dynamics by considering instructive synthetic examples of simplicial complexes. The proposed higher-order diffusion processes can be adopted in real scenarios when we want to consider higher-order diffusion displaying non-trivial frustration effects due to conflicting directionalities of the incident simplices.

Acute Aromatase Inhibition Impairs Neural and Behavioral Auditory Scene Analysis in Zebra Finches.

Auditory perception can be significantly disrupted by noise. To discriminate sounds from noise, auditory scene analysis (ASA) extracts the functionally relevant sounds from acoustic input. The zebra finch communicates in noisy environments. Neurons in their secondary auditory pallial cortex (caudomedial nidopallium, NCM) can encode song from background chorus, or scenes, and this capacity may aid behavioral ASA. Furthermore, song processing is modulated by the rapid synthesis of neuroestrogens when hearing conspecific song. To examine whether neuroestrogens support neural and behavioral ASA in both sexes, we retrodialyzed fadrozole (aromatase inhibitor, FAD) and recorded in vivo awake extracellular NCM responses to songs and scenes. We found that FAD affected neural encoding of songs by decreasing responsiveness and timing reliability in inhibitory (narrow-spiking), but not in excitatory (broad-spiking) neurons. Congruently, FAD decreased neural encoding of songs in scenes for both cell types, particularly in females. Behaviorally, we trained birds using operant conditioning and tested their ability to detect songs in scenes after administering FAD orally or injected bilaterally into NCM. Oral FAD increased response bias and decreased correct rejections in females, but not in males. FAD in NCM did not affect performance. Thus, FAD in the NCM impaired neuronal ASA but that did not lead to behavioral disruption suggesting the existence of resilience or compensatory responses. Moreover, impaired performance after systemic FAD suggests involvement of other aromatase-rich networks outside the auditory pathway in ASA. This work highlights how transient estrogen synthesis disruption can modulate higher-order processing in an animal model of vocal communication.

Multi-channel high-order network representation learning research.

The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling.

The neural oscillations serving task switching are altered in cannabis users.

Regular cannabis is known to impact higher-order cognitive processes such as attention, but far less is known regarding cognitive flexibility, a component of executive function. Moreover, whether such changes are related to aberrations in the neural oscillatory dynamics serving flexibility remains poorly understood. Quantify the neural oscillatory dynamics serving cognitive flexibility by having participants complete a task-switching paradigm during magnetoencephalography (MEG). Probe whole-brain maps to identify alterations in chronic cannabis users relative to nonusers and determine how these alterations relate to the degree of cannabis use involvement. In all, 25 chronic cannabis users and 30 demographically matched nonuser controls completed neuropsychological testing, an interview regarding their substance use, a urinalysis, and a task switch paradigm during MEG. Time-frequency windows of interest were identified using a data-driven statistical approach and these were imaged using a beamformer. Whole-brain neural switch cost maps were computed by subtracting the oscillatory maps of the no-switch condition from the switch condition per participant. These were examined for group differences. Cannabis users had weaker theta switch cost responses in the dorsolateral and dorsomedial prefrontal cortices, while nonusers showed the typical pattern of greater recruitment during switch relative to no switch trials. In addition, theta activity in the dorsomedial prefrontal cortex was significantly correlated with cannabis use involvement. Cannabis users exhibited altered theta switch cost activity compared to nonusers in prefrontal cortical regions, which are critical for cognitive flexibility. This activity scaled with cannabis use involvement, indicating a link between cannabis use and aberrant oscillatory activity underlying cognitive flexibility.