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To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. This work was supported by the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud. We gratefully acknowledge support by Digital Europe Grant TEF-Health # 101100700 (P.R.); H2020 Research and Innovation Action Grant Human Brain Project SGA2 785907 (P.R.); H2020 Research and Innovation Action Grant Human Brain Project SGA3 945539 (P.R.); H2020 Research and Innovation Action Grant Interactive Computing E-Infrastructure for the Human Brain Project ICEI 800858 (P.R.); H2020 Research and Innovation Action Grant EOSC VirtualBrainCloud 826421 (P.R.); H2020 Research and Innovation Action Grant AISN 101057655 (P.R.); H2020 Research Infrastructures Grant EBRAINS-PREP 101079717 (P.R.); H2020 European Innovation Council PHRASE 101058240 (P.R.); H2020 Research Infrastructures Grant EBRAIN-Health 101058516 (P.R.); H2020 European Research Council Grant ERC BrainModes 683049 (P.R.); JPND ERA PerMed PatternCog 2522FSB904 (P.R.); Berlin Institute of Health & Foundation Charité (P.R.); Johanna Quandt Excellence Initiative (P.R.); German Research Foundation SFB 1436 (project ID 425899996) (P.R.); German Research Foundation SFB 1315 (project ID 327654276) (P.R.); German Research Foundation SFB 936 (project ID 178316478) (P.R.); German Research Foundation SFB-TRR 295 (project ID 424778381) (P.R.); German Research Foundation SPP Computational Connectomics RI 2073/6-1, RI 2073/10-2, RI 2073/9-1 (P.R.).

Structure and flexibility in cortical representations of odour space Data to replicate analyses in Pashkovski et al. 2020 Nature https://doi.org/10.1038/s41586-020-2451-1 ## Abstract The cortex organizes sensory information to enable discrimination and generalization. As systematic representations of chemical odour space have notyet been described in the olfactory cortex, it remains unclear how odour relationships are encoded to place chemically distinct but similar odours, such as lemon and orange, into perceptual categories, such as citrus. Here, by combining chemoinformatics and multiphoton imaging in the mouse, we show that both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odour relationships through correlated patterns of activity. However, cortical odour codes differ from those in the bulb: cortex more strongly clusters together representations for related odours, selectively rewrites pairwise odour relationships, and better matches odour perception. The bulb-to-cortex transformation depends on the associative network originating within the piriform cortex, and can be reshaped by passive odour experience. Thus, cortex actively builds a structured representation of chemical odour space that highlights odour relationships; this representation is similar across individuals but remains plastic, suggesting a means through which the olfactory system can assign related odour cues to common and yet personalized percepts. ## Supplementary data * `pseudopopulations.h5` contains pseudopopulations of the mean responses for trials x ROIs x odors * `chemical_descriptors.h5` is a pandas dataframe containing chemical features for each odorant. # Code For an example notebook using these data, please see the associated GitHub repo: https://github.com/dattalab/cortical-representations-odour-space. &nbsp

Task graphs provide a simple way to describe scientific workflows (sets of tasks with dependencies) that can be executed on both HPC clusters and in the cloud. An important aspect of executing such graphs is the used scheduling algorithm. Many scheduling heuristics have been proposed in existing works; nevertheless, they are often tested in oversimplified environments. We provide an extensible simulation environment designed for prototyping and benchmarking task schedulers, which contains implementations of various scheduling algorithms and is open-sourced, in order to be fully reproducible. We use this environment to perform a comprehensive analysis of workflow scheduling algorithms with a focus on quantifying the effect of scheduling challenges that have so far been mostly neglected, such as delays between scheduler invocations or partially unknown task durations. Our results indicate that network models used by many previous works might produce results that are off by an order of magnitude in comparison to a more realistic model. Additionally, we show that certain implementation details of scheduling algorithms which are often neglected can have a large effect on the scheduler’s performance, and they should thus be described in great detail to enable proper evaluation.

A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Here we decompose functional MRI signals from pathological and pharmacologically-induced perturbations of consciousness into distributed patterns of structure-function dependence across scales: the harmonic modes of the human structural connectome. `We show that structure-function coupling is a generalisable indicator of consciousness that is under bi-directional neuromodulatory control. We find increased structure-function coupling across scales during loss of consciousness, whether due to anaesthesia or brain injury, capable of discriminating between behaviourally indistinguishable sub-categories of brain-injured patients, tracking the presence of covert consciousness. The opposite harmonic signature characterises the altered state induced by LSD or ketamine, reflecting psychedelic-induced decoupling of brain function from structure and correlating with physiological and subjective scores. Overall, connectome harmonic decomposition reveals how neuromodulation and the network architecture of the human connectome jointly shape consciousness and distributed functional activation across scales., AIL, JV and PAMM would like to thank Lena Dorfschmidt for co-organising OxBridge BrainHack 2019, where this work began. We also thank all volunteers and patients who provided data. This work was supported by grants from The Wellcome Trust Research Training Fellowship (grant no. 083660/Z/07/Z), Raymond and Beverly Sackler Studentship, and the Cambridge Commonwealth Trust [RA]; the UK Medical Research Council (U.1055.01.002.00001.01) [JDP]; The James S. McDonnell Foundation [JDP]; the Canadian Institute for Advanced Research (CIFAR; grant RCZB/072 RG93193) [to DKM and EAS]; The National Institute for Health Research (NIHR, UK), Cambridge Biomedical Research Centre and NIHR Senior Investigator Awards [JDP and DKM]; The British Oxygen Professorship of the Royal College of Anaesthetists [DKM]; The Stephen Erskine Fellowship, Queens’ College, University of Cambridge [EAS]; The Evelyn Trust, Cambridge and the EoE CLAHRC fellowship [JA]; The Gates Cambridge Trust [AIL]; The Cambridge International Trust and the Howard Sidney Sussex Studentship [MMC]; The Oon Khye Beng Ch'Hia Tsio Studentship for Research in Preventive Medicine, Downing College, University of Cambridge [IP]; The Wellcome Trust (grant no. 210920/Z/18/Z) [PAMM]; The European Research Council Consolidator Grant CAREGIVING (615539) [MLK and SA]; The Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117) [MLK, SA and JV]; The Centre for Eudaimonia and Human Flourishing, funded by the Pettit and Carlsberg Foundations [MLK]; The Imperial College President’s Scholarship [LR]; The Alex Mosley Charitable Trust [RLCH]; The ketamine study was funded by the Bernard Wolfe Health Neuroscience Fund and the Wellcome Trust. The original LSD study received support from a Crowd Funding Campaign and the Beckley Foundation, as part of the Beckley-Imperial Research Programme. The research was also supported by the NIHR Brain Injury Healthcare Technology Co-operative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge. Data used to obtain the human connectome were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.

В известных моделях календарного планирования наличие нескладируемых (возобновляемых) ресурсов полагается заранее заданным во всех временных интервалах, т. е. предполагается их априорное распределение до построения расписания выполнения проекта. Общеизвестное определение нескладируемых ресурсов как ненакапливаемых (типа «мощность»), неиспользование которых приводит к их потере, неполностью отражает их специфику. В статье рассмотрена формализация ресурсных условий в трех задачах построения расписаний работ: минимизация длительности цикла при ограниченных ресурсах; минимизация несбалансированности ресурсов при известном их количестве в интервалах планового периода; распределение ресурсов по критерию минимизации дисбаланса с возможностью перераспределения нескладируемых ресурсов. Предложена формализация условий целочисленной линейной модели построения расписания работ с переменными нескладируемыми ресурсами, минимизирующего дисбаланс при заданных директивных сроках завершения проектов., In complicated complexes of operations scheduling renewable resources are assumed by constants that does not always agree with practice of management. Determining of renewable resources as not stored (type “power”) which non-use leads to their loss, does not fully reflect their specificity. Formalizing of redistribution of renewable resources is linked to representation conditions of their usage in models. Redistribution of resources is considered on an example of network model for minimizing an unbalance of resources at set directive times for completion scheduling.

We set to investigate what sample sizes are roughly needed for reliably fitting cross-sectional network models to binary and continuous data using state-of-the-art network methodologies, using a simulation study.

The goal of this study is to synthesize the evidence on the psychometric properties of Dweck’s (1999) mindset scale across several primary studies. Specifically, this evidence contains: (a) Item-item correlations and possible moderators thereof; (b) Factor structure of the mindset scale (e.g., one- vs. multiple-factors models) and network structure; (c) Possible subgroup differences in the factor structure; (d) Reliability coefficients. By means of item-level meta-analysis, this study synthesizes the aforementioned evidence on the psychometric properties of the mindset scale (focusing on aspects of validity and reliability) across primary studies, quantifies between-study heterogeneity, and tests possible moderator effects of study, sample, and measurement characteristics.

Background. Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.Methods. We estimated seven regularized mixed graphical models in the NESDA data (N=2321) to explore shared variances among 1) depression severity, modeled via depression sum-score, 9 DSM-5 symptoms, or 28 individual depressive symptoms; 2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); 3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.Results. The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al. (2016), CRP was associated with ‘sleep problems’, ‘energy level’, and ‘weight/appetite changes’; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms ‘insomnia, ‘hypersomnia’, and ‘aches and pain’ showed unique positive relations to all inflammatory markers.Conclusions. We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: what are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? (3) And how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) Heterogeneity of samples studied with network analytic models; and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood, and promises advances in understanding psychopathology both at the nomothetic and idiographic level.

Advanced Theory of Mind (AToM) involves the comprehension of complex mental states. Discussion about the nature of development of AToM exists between the conceptual change hypothesis – claiming AToM ability as a result of a qualitative change, and the complexity-only hypothesis – describing AToM ability as a sole result of increased general information processing skills. We argue AToM to be studied in a mutualistic way and propose a framework in which (1) an indication for either hypothesis can be inferred and (2) the kind and strength of involved processes within different age-groups can be identified. Kind and strength will be assessed by Kievit’s (2020) quantitative- and qualitative sensitive periods (QtSP and QlSP respectively). The identification of periods in which associations between variables are stronger than before and thereafter, are QtSP’s (Kievit, 2020). The identification of the kind of processes involved in a timeframe is called the QlSP for those processes (Kievit, 2020), since specific abilities might be crucial to develop some ability in one moment, but not in another. Identifying a QtSP wherein AToM associations are generally strongest could serve as an indication for conceptual change. Contrarily, the absence of such a period and the importance of more general information-processing skills in QlSP’s, points towards evidence for the complexity-only hypothesis.We hypothesize AToM to develop in a locally conceptual way, meaning a qualitative change for different AToM composites at different time-points. The model and our hypothesis will be tested on two datasets measuring AToM on children between age 5 and 10 using network analytic measures and simulation studies will be conducted for hypothesis generation. Note: for a more detailed project description, we refer to our project proposal.

40-minute talk on network theory and models at University of Cambridge, with a focus on psychopathology research. Includes an overview of theoretical, applied, and methodological papers, and links to tutorials. Thanks to Rogier Kievit and the Cognition & Brain Science Unit for the invitation!

The 2-day network workshop starts with a conceptual introduction on why items in psychological data tend to co-occur, and what this implies about the constructs we work with. This is followed by an introduction to social and psychological network models; an overview of the network literature in psychopathology (the field where network psychometric models have been used most over the last years); and a summary of important topics (centrality, comorbidity, early warning signals). The first group of statistical models we learn are network models in cross-sectional data. We will use the free statistical environment R to learn the basics about (1) network estimation, (2) network inference, and (3) network accuracy. We will finish this section with some advanced topics and methods, such as network comparisons, modeling of different types of variables, and considerations about causality. The statistical focus of day 2 is on dynamic time-series models: how do variables impact on each other over time? After an introduction into the general modeling framework, we learn to estimate network models for n=1 and n larger than 1 time-series data, followed by a discussion of some common problems and advanced techniques. We round up the workshop with a practical session where workshop participants learn to apply the knowledge to several datasets.

This project is aimed at quantifying gender differences in teachers' readiness for online teaching and learning (OTL). OTL readiness is conceptualized and assessed via a multidimensional framework that included self-efficacy beliefs, teaching practices, and support. Specifically, we address the following research questions: (RQ1) To what extent do teachers' levels of OTL readiness differ across gender? (RQ2) To what extent does the measurement of OTL readiness function similarly across gender? (RQ3) To what extent do the relations among readiness constructs and indicators differ across gender?

The European rabbit (Oryctolagus cuniculus) is one of the main invasive species in Chile, where it became naturalized ca. 150 years ago. Their high reproductive capacity, lack of specialist predators, and great adaptability favored the settlement of rabbits in diverse mainland and island ecosystems of the country. Recently, rabbits have become central players in semi-arid ecosystems, such as those represented in Las Chinchillas National Reserve in north-central Chile. We undertook to analyze the place and role of rabbits in the food web of that Reserve, based on a bibliographic review and long-term annual data gathered from 1987 until 2022 (36 years). Results showed that the network comprised 77 species, where 69% were primary producers (plants), 18% were mid-level consumers (herbivores), and 13% were top-level consumers (predators). The most connected species in the food web was the rabbit, which positively or negatively affected the species interacting with it. Predators such as Galictis cuja, Geranoaetus polyosoma, Leopardus colocolo, and Puma concolor, and the scavenger Vultur gryphus, could be negatively affected by an eventual decrease (natural or human-caused) in the rabbit population of the Reserve. To the contrary, primary producers such as Oxalis perdicaria, Plantago hispidula, Schizanthus parvulus, Senna cumminggi, and Tropaeolum azureum could be positively affected by an increase in their biomass in response to a decrease in rabbits, favoring native rodents. We consider that analyzing the rabbit-centered food web and its impacts on native interacting species allows a better understanding of the relevance of invasive species in the local community, providing conceptual tools for rabbit management.

Purpose This paper aims to analyze how knowledge networks can be configured within a value chain and provide evidence of the coexistence of multiple knowledge networks in the same value chain. Design/methodology/approach The empirical setting is the Conegliano Valdobbiadene Prosecco Superiore DOCG wine cluster in the Veneto region of Northeast Italy. Data was collected through the administration by telephone of a semi-structured questionnaire to 37 oenologists, sales managers, production managers and owners of bottling companies in the district. The authors used social network analysis tools to map knowledge networks in the Prosecco cluster. Findings The results shed light on the importance of singling out knowledge networks in clusters at the value chain level to aid practitioners and researchers in this field. In fact, this research proves the existence of knowledge networks specificities related to the various phases of the production process. Research limitations/implications This study has certain limitations. The most relevant is connected to the choice to limit the analysis to a specific cluster. Future research might extend this type of analysis to multiple clusters in different locations. Practical implications The authors explain that in the cluster they studied, internationalization, as a common objective, might be made easier if firms could establish a more developed sales knowledge network. Social implications The relational approach to value chain enables disentangling specific roles of each actors. The social dimension of the value chain is taken in consideration. Originality/value The authors show that a firm operating in the wine industry can have different knowledge networks in the same value chain. This work adds to previous literature on knowledge networks in clusters by shedding light on an important, but still understudied aspect in the cluster functioning. Knowledge diffusion in clusters is not only uneven but is also value chain stage specific. By intersecting literature on knowledge networks, value chain and cluster research, the authors proposed a new perspective of analysis of the wine industry.

In the present study, we tested the common assumption that teachers with more experience consider themselves better prepared for online teaching and learning (OTL). Utilizing an international data set of 731 teachers in higher education at the beginning of the COVID-19 pandemic, we performed structural equation and network modeling to quantify the experience-readiness relationship. In contrast to the linearity assumption “the more experienced, the better prepared”, we found robust evidence for a curvilinear relationship. Teachers’ readiness for OTL increased first and then decreased with more experience—this applied especially to the self-efficacy dimension of readiness. Further analyses suggested that the experience-readiness relationship does not only exist at the level of aggregated constructs but also at the level of indicators, that is, specific areas of knowledge, teaching, and support. Hence, describing the experience-readiness relationship requires a measurement perspective that accounts for possible differential functioning of the readiness measures. We argue that both novice and experienced teachers in higher education could benefit from experience-appropriate, pedagogical, and content-related support programs for OTL.

The need to improve the efficiency of transport infrastructure, which is an important subsystem of urban services as a determinant of the quality of life of the city’s population, has been substantiated. The factors that determine the quality of the urban transport system, the features of urban transport have been highlighted. Transport infrastructure development in Russia has been analysed. It has been proved that in the conditions of the formation of the digital economy, artificial intelligence systems are an effective tool for decision-making. In the formation of intelligent systems for managing urban transport flows, the use of network models has been proposed, for which mathematical methods are necessary to obtain not only point, but also interval estimates of the model parameters, taking into account a priori uncertainty.

© 2020 Annual Review of Statistics and Its Application. All rights reserved. Algebraic statistics uses tools from algebra (especially from multilinear algebra, commutative algebra, and computational algebra), geometry, and combinatorics to provide insight into knotty problems in mathematical statistics. In this review, we illustrate this on three problems related to networks: network models for relational data, causal structure discovery, and phylogenetics. For each problem, we give an overview of recent results in algebraic statistics, with emphasis on the statistical achievements made possible by these tools and their practical relevance for applications to other scientific disciplines.

All mammalian mothers form some sort of caring bond with their infants that is crucial to the development of their offspring. The Pup Retrieval Test (PRT) is the leading procedure to assess pup-directed maternal care in laboratory rodents, used in a wide range of basic and preclinical research applications. Most PRT protocols require manual scoring, which is prone to bias and spatial and temporal inaccuracies. This study proposes a novel procedure using machine learning algorithms to enable reliable assessment of PRT performance. Automated tracking of a dam and one pup was established in DeepLabCut and was combined with automated behavioral classification of “maternal approach”, “carrying” and “digging” in Simple Behavioral Analysis (SimBA). Our automated procedure estimated retrieval success with an accuracy of 86.7%, whereas accuracies of “approach”, “carry” and “digging” were estimated at respectively 99.3%, 98.6% and 85.0%. We provide an open-source, step-by-step protocol for automated PRT assessment, which aims to increase reproducibility and reliability, and can be easily shared and distributed. ispartof: Scientific Reports vol:12 issue:1 pages:1-9 ispartof: location:England status: published

Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of nodules in CT images is vital for lung cancer diagnosis. A 3D automatic detection system of pulmonary nodules based on multi-scale attention networks is proposed in this paper to use multi-scale features of nodules and avoid network over-fitting problems. The system consists of two parts, nodule candidate detection (determining the locations of candidate nodules), false positive reduction (minimizing the number of false positive nodules). Specifically, with Res2Net structure, using pre-activation operation and convolutional quadruplet attention module, the 3D multi-scale attention block is designed. It makes full use of multi-scale information of pulmonary nodules by extracting multi-scale features at a granular level and alleviates over-fitting by pre-activation. The U-Net-like encoder-decoder structure is combined with multi-scale attention blocks as the backbone network of Faster R-CNN for detection of candidate nodules. Then a 3D deep convolutional neural network based on multi-scale attention blocks is designed for false positive reduction. The extensive experiments on LUNA16 and TianChi competition datasets demonstrate that the proposed approach can effectively improve the detection sensitivity and control the number of false positive nodules, which has clinical application value.

We acknowledge support from the W. M. Keck Foundation, and the US Israel Binational Science Foundation (BSF Grant 2020020). We also thank Dr. Sergi Garcia-Retortillo for stimulating discussions and helpful comments on the manuscript., The current paradigm in brain research focuses on individual brain rhythms, their spatiotemporal organization, and specific pairwise interactions in association with physiological states, cognitive functions, and pathological conditions. Here we propose a conceptually different approach to understanding physiologic function as emerging behavior from communications among distinct brain rhythms. We hypothesize that all brain rhythms coordinate as a network to generate states and facilitate functions. We analyze healthy subjects during rest, exercise, and cognitive tasks and show that synchronous modulation in the microarchitecture of brain rhythms mediates their cross-communications. We discover that brain rhythms interact through an ensemble of coupling forms, universally observed across cortical areas, uniquely defining each physiological state. We demonstrate that a dynamic network regulates the collective behavior of brain rhythms and that network topology and links strength hierarchically reorganize with transitions across states, indicating that brain-rhythm interactions play an essential role in generating physiological states and cognition., W.M. Keck Foundation, US-Israel Binational Science Foundation 2020020

Fast and accurate threat detection is critical for animal survival. Reducing perceptual ambiguity by integrating multiple sources of sensory information can enhance perception and reduce response latency. However, studies addressing the link between behavioral correlates of multisensory integration and its underlying neural basis are rare. Fish that detect an urgent threat escape with an explosive behavior known as C-start. The C-start is driven by an identified neural circuit centered on the Mauthner cell, an identified neuron capable of triggering escapes in response to visual and auditory stimuli. Here we demonstrate that goldfish can integrate visual looms and brief auditory stimuli to increase C-start probability. This multisensory enhancement is inversely correlated to the salience of the stimuli, with weaker auditory cues producing a proportionally stronger multisensory effect. We also show that multisensory stimuli reduced C-start response latency, with most escapes locked to the presentation of the auditory cue. We make a direct link between behavioral data and its underlying neural mechanism by reproducing the behavioral data with an integrate-and-fire computational model of the Mauthner cell. This model of the Mauthner cell circuit suggests that excitatory inputs integrated at the soma are key elements in multisensory decision making during fast C-start escapes. This provides a simple but powerful mechanism to enhance threat detection and survival.

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n = 20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains raw EEG and behavioral data, pre-processed EEG of single trials in each condition, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities., Measurement(s)visual evoked potentials • brain activity measurement • diffusion weighted imagesTechnology Type(s)high-density electroencephalography • magnetic resonance imagingFactor Type(s)visual stimulusSample Characteristic - OrganismHomo sapiensSample Characteristic - Environmentlaboratory environment Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.17074685

Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention., Noise correlations in the neocortex change dynamically with cognitive states. Here the authors show how heterogeneous spatial patterns of noise correlations emerge through interactions of cortical On-Off dynamics, connectivity, and attention.

Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (

Functional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functional connectivity analysis, we assess altered functional connectivity in overweight individuals relative to healthy weight controls along the brain hierarchy. Seeding from the brain regions associated with obesity phenotypes, we conduct stepwise connectivity analysis at different step distances and compare functional degrees between the groups. We find strong functional connectivity in the somatomotor and prefrontal cortices in both groups, and both converge to transmodal systems, including frontoparietal and default-mode networks, as the number of steps increased. Conversely, compared with the healthy weight group, overweight individuals show a marked decrease in functional degree in somatosensory and attention networks across the steps, whereas visual and limbic networks show an increasing trend. Associating functional degree with eating behaviors, we observe negative associations between functional degrees in sensory networks and hunger and disinhibition-related behaviors. Our findings suggest that overweight individuals show disrupted functional network organization along the hierarchical axis of the brain and these results provide insights for behavioral associations., Hyebin Lee et al. use a stepwise functional connectivity technique to study differences in functional connectivity between overweight and healthy weight individuals. They find disrupted functional network organization along the hierarchical axis of the brain in overweight individuals that relates to behavioral traits

Статья посвящена выявлению архитектуры коммуникационных каналов и топологии информационных потоков в системе общественных взаимодействий. Методология включает историко-генетический анализ дискурса в условиях пандемии и анализ ряда кейсов, иллюстрирующих выдвижение исследовательских гипотез. Построена архитектура кризисных коммуникаций в период пандемии в российском обществе с учётом деятельности государства и индивидуального и институционального социального предпринимательства., The article is devoted to identifying the architecture of communication channels and the topology of information flows in the system of social interactions. The methodology includes a historical and genetic analysis of discourse in a pandemic and an analysis of a number of cases illustrating the advancement of research hypotheses. The architecture of crisis communications during a pandemic in Russian society has been formulated and built, taking into account the activities of the state and individual and institutional social entrepreneurship

This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross-sectional units are observed over a short period of time and cross-sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modeling the decay function in network neighborliness (e.g., by disentangling the importance of rings of neighbors) or towards allowing for several channels of interdependence whose relative importance is unknown ex ante. Besides the flexible parameterization of cross-sectional dependence, the approach allows for simultaneity of the equations. These features should make the approach interesting for applications in a host of contexts involving structural and reduced-form models of multivariate choice problems at micro-, meso-, and macro-economic levels. The paper outlines the estimation approach, illustrates its suitability by simulation examples, and provides an application to study exporting and foreign ownership among potentially interdependent firms in the specialized and transport machinery sector in the province of Guangdong., Journal of Applied Econometrics, 37 (7), ISSN:0883-7252, ISSN:1099-1255, ISSN:0021-8901, ISSN:1365-2664

We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1-2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1-8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1-8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to 3 connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.

This paper develops the analysis of properties of the cellular automata class introduced by the authors. It is assumed that the set of automaton cells is finite and forms a closed lattice, and there are two states for each automaton cell. We consider a new concept. This concept is the average velocity of a cellular automaton, which characterizes the average intensity of changes in the states of the automaton’s cells for a given initial state. The automaton velocity is equal to 1 if the state of any cell changes at each step. The spectrum of average velocities of a cellular automaton is the set of average velocities for different initial states. Since the state space is finite, the automaton, starting from a certain moment of time, is in periodically repeating states of a cycle, and thus, the research of the velocity spectrum is related to the problem of studying the set of the automaton cycles. For elementary cellular automata, the introduced class consists of a subclass of automata such that the conjugation trasformation of an automaton is the automaton itself (Subclass A) or the reflection of the automaton (Subclass B). For this class, it is proved that the spectrum of the automaton contains the value v0 if and only if the spectrum of the complementary automaton contains the value 1−v0 (the sum of the index of elementary cellular automaton and the complementary automaton is 255). For automata of Subclasses A and B, the set of cycles and the velocity spectrum are studied. For Subclass A, a theorem has been proved such that in accordance with this theorem, if two automata complementary to each other start evolving in the same initial state, then the sum of their average velocities is equal to 1. This theorem for Subclass A is generalized to cellular automata, invariant under the conjugation transformation, of more general type than elementary automata. Generalizations of the theorem have been given for the class of one-dimensional cellular automata with a neighborhood containing 2r+1 cells (the next state of the cell depends on the present states of this cell, r cells on the left and r cells on the right) and for some traditionally considered classes of two-dimensional automata. Some elementary cellular automata belonging to the class considered in the paper can be interpreted as transport models. The properties of the spectra for these automata are studied and compared with the properties of elementary cellular automata not invariant under the considered transformations and can also be interpreted as transport models. The analytical results obtained for these simple models can be used to study the qualitative properties and limiting behavior of more complex transport models.

Tau is a hallmark pathology of Alzheimer's disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β-amyloid (Aβ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer's disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain Aβ is present, but regions with greater Aβ burden show greater tau than predicted by connectivity patterns, suggesting a role of Aβ in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain Aβ.

Visuospatial working memory (VSWM) involves cortical regions along the dorsal visual pathway, which are topographically organized with respect to the visual space. However, it remains unclear how such functional organization may constrain VSWM behavior across space and time. Here, we systematically mapped VSWM performance across the 2-dimensional (2D) space in various retention intervals in human subjects using the memory-guided and visually guided saccade tasks in two experiments. Relative to visually guided saccades, memory-guided saccades showed significant increases in unsystematic errors, or response variability, with increasing target eccentricity (3°–13° of visual angle). Unsystematic errors also increased with increasing delay (1.5–3 s, Experiment 1; 0.5–5 s, Experiment 2), while there was little or no interaction between delay and eccentricity. Continuous bump attractor modeling suggested neurophysiological and functional organization factors in the increasing unsystematic errors in VSWM across space and time. These findings indicate that: (1) VSWM representation may be limited by the functional topology of the visual pathway for the 2D space; (2) Unsystematic errors may reflect accumulated noise from memory maintenance while systematic errors may originate from non-mnemonic processes such as noisy sensorimotor transformation; (3) There may be independent mechanisms supporting the spatial and temporal processing of VSWM.

Propagation of activity in spatially structured neuronal networks has been observed in awake, anesthetized, and sleeping brains. How these wave patterns emerge and organize across brain structures, and how network connectivity affects spatiotemporal neural activity remains unclear. Here, we develop a computational model of a two-dimensional thalamocortical network, which gives rise to emergent traveling waves similar to those observed experimentally. We illustrate how spontaneous and evoked oscillatory activity in space and time emerge using a closed-loop thalamocortical architecture, sustaining smooth waves in the cortex and staggered waves in the thalamus. We further show that intracortical and thalamocortical network connectivity, cortical excitation/inhibition balance, and thalamocortical or corticothalamic delay can independently or jointly change the spatiotemporal patterns (radial, planar and rotating waves) and characteristics (speed, direction, and frequency) of cortical and thalamic traveling waves. Computer simulations predict that increased thalamic inhibition induces slower cortical frequencies and that enhanced cortical excitation increases traveling wave speed and frequency. Overall, our results provide insight into the genesis and sustainability of thalamocortical spatiotemporal patterns, showing how simple synaptic alterations cause varied spontaneous and evoked wave patterns. Our model and simulations highlight the need for spatially spread neural recordings to uncover critical circuit mechanisms for brain functions.

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis—from genes to cognition., The formation of large-scale brain networks represents crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. Here, the authors use generative network modelling to provide a computational framework for understanding neurodevelopmental diversity.

Control processes associated with working memory play a central role in human cognition, but their underlying dynamic brain circuit mechanisms are poorly understood. Here we use system identification, network science, stability analysis, and control theory to probe functional circuit dynamics during working memory task performance. Our results show that dynamic signaling between distributed brain areas encompassing the salience (SN), fronto-parietal (FPN), and default mode networks can distinguish between working memory load and predict performance. Network analysis of directed causal influences suggests the anterior insula node of the SN and dorsolateral prefrontal cortex node of the FPN are causal outflow and inflow hubs, respectively. Network controllability decreases with working memory load and SN nodes show the highest functional controllability. Our findings reveal dissociable roles of the SN and FPN in systems control and provide novel insights into dynamic circuit mechanisms by which cognitive control circuits operate asymmetrically during cognition., Working memory is a foundational component of cognition, but its mechanisms are poorly understood. Using a large sample of participants, this study identifies asymmetric dynamic interactions in cognitive control circuits, and their load-dependent network properties including controllability.

The superior colliculus (SC) receives diverse and robust cortical inputs to drive a range of cognitive and sensorimotor behaviors. However, it remains unclear how descending cortical input arising from higher-order associative areas coordinate with SC sensorimotor networks to influence its outputs. Here, we construct a comprehensive map of all cortico-tectal projections and identify four collicular zones with differential cortical inputs: medial (SC.m), centromedial (SC.cm), centrolateral (SC.cl) and lateral (SC.l). Further, we delineate the distinctive brain-wide input/output organization of each collicular zone, assemble multiple parallel cortico-tecto-thalamic subnetworks, and identify the somatotopic map in the SC that displays distinguishable spatial properties from the somatotopic maps in the neocortex and basal ganglia. Finally, we characterize interactions between those cortico-tecto-thalamic and cortico-basal ganglia-thalamic subnetworks. This study provides a structural basis for understanding how SC is involved in integrating different sensory modalities, translating sensory information to motor command, and coordinating different actions in goal-directed behaviors., The superior colliculus (SC) receives diverse cortical inputs to drive many behaviors. Here, based on comprehensive mapping of cortico-tectal projections, the authors refined the superior colliculus into medial, centromedial, centrolateral, and lateral zones, and characterized the input-output connectivity and morphology of neurons in each zone that serve the role of SC in goal-directed behaviors.

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Humans and many animals have the ability to assess the confidence of their decisions. However, little is known about the underlying neural substrate and mechanism. In this study we propose a computational model consisting of a group of ’confidence neurons’ with adaptation, which are able to assess the confidence of decisions by detecting the slope of ramping activities of decision neurons. The simulated activities of ’confidence neurons’ in our simple model capture the typical features of confidence observed in humans and animals experiments. Our results indicate that confidence could be online formed along with the decision formation, and the adaptation properties could be used to monitor the formation of confidence during the decision making.

Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

A number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highlight the importance of using the appropriate network connectivity to investigate epileptiform activity with computational models.

There is a growing body of literature using functional MRI to study the acute and long-term effects of concussion on functional brain networks. To date, studies have largely focused on changes in pairwise connectivity strength between brain regions. Less is known about how concussion affects whole-brain network topology, particularly the “small-world” organization which facilitates efficient communication at both local and global scales. The present study addressed this knowledge gap by measuring local and global efficiency of 26 concussed athletes at acute injury, return to play (RTP) and one year post-RTP, along with a cohort of 167 athletic controls. On average, concussed athletes showed no alterations in local efficiency but had elevated global efficiency at acute injury, which had resolved by RTP. Athletes with atypically long recovery, however, had reduced global efficiency at 1 year post-RTP, suggesting long-term functional abnormalities for this subgroup. Analyses of nodal efficiency further indicated that global network changes were driven by high-efficiency visual and sensorimotor regions and low-efficiency frontal and subcortical regions. This study provides evidence that concussion causes subtle acute and long-term changes in the small-world organization of the brain, with effects that are related to the clinical profile of recovery.

Though Parkinson’s disease is primarily defined as a movement disorder, it is also characterized by a range of non-motor symptoms, including cognitive decline. The onset and progression of cognitive decline in individuals with Parkinson’s disease is variable, and the neurobiological mechanisms that contribute to, or protect against, cognitive decline in Parkinson’s disease are poorly understood. Using resting-state functional magnetic resonance imaging data collected from individuals with Parkinson’s disease with and without cognitive decline, we examined the relationship between topological brain-network resilience and cognition in Parkinson’s disease. By leveraging network attack analyses, we demonstrate that relative to individuals with Parkinson’s disease experiencing cognitive decline, the frontoparietal network in cognitively stable individuals with Parkinson’s disease is significantly more resilient to network perturbation. Our findings suggest that the topological robustness of the frontoparietal network is associated with the absence of cognitive decline in individuals with Parkinson’s disease., Arianna Cascone et al. examine the relationship between frontoparietal brain network resilience and cognitive decline in Parkinson’s disease (PD). Their results suggest that individuals with PD and cognitive decline exhibit reduced tolerance to network "attacks" or perturbation in the frontoparietal network, compared to both individuals with PD with normal cognition and healthy, non-PD controls.

Evolution of the direct, monosynaptic connection from the primary motor cortex to the spinal cord parallels acquisition of hand dexterity and lateralization of hand preference. In non-human mammals, the indirect, multi-synaptic connections between the bilateral primary motor cortices and the spinal cord also participates in controlling dexterous hand movement. However, it remains unknown how the direct and indirect corticospinal pathways work in concert to control unilateral hand movement with lateralized preference in humans. Here we demonstrated the asymmetric functional organization of the two corticospinal networks, by combining network modelling and simultaneous functional magnetic resonance imaging techniques of the brain and the spinal cord. Moreover, we also found that the degree of the involvement of the two corticospinal networks paralleled lateralization of hand preference. The present results pointed to the functionally lateralized motor nervous system that underlies the behavioral asymmetry of handedness in humans., 新規MRI技術で利き手の神経制御メカニズムを解明 --手指運動中の脳・脊髄機能結合パターンの左右差を世界で初めて計測--. 京都大学プレスリリース. 2022-08-08.

Using brain atlases to localize regions of interest is a required for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization and specification. The purpose of standardization and specification is to ensure consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Consequently, researchers are often confronted with limited knowledge about a given atlas’s organization, which make analytic comparisons more difficult. To fill this gap, we consolidate an extensive selection of popular human brain atlases into a single, curated open-source library, where they are stored following a standardized protocol with accompanying metadata. We propose that this protocol serves as the basis for storing future atlases. To demonstrate the utility of storing and standardizing these atlases following a common protocol, we conduct an experiment using data from the Healthy Brain Network whereby we quantify the statistical dependence of each atlas label on several key phenotypic variables. The repository containing the atlases, the specification, as well as relevant transformation functions is available at https://neurodata.io/mri

The mechanisms underlying circuit dysfunctions in schizophrenia (SCZ) remain poorly understood. Auditory steady-state responses (ASSRs), especially in the gamma and beta band, have been suggested as a potential biomarker for SCZ. While the reduction of 40 Hz power for 40 Hz drive has been well established and replicated in SCZ patients, studies are inconclusive when it comes to an increase in 20 Hz power during 40 Hz drive. There might be several factors explaining the inconsistencies, including differences in the sensitivity of the recording modality (EEG vs MEG), differences in stimuli (click-trains vs amplitude-modulated tones) and large differences in the amplitude of the stimuli. Here, we used a computational model of ASSR deficits in SCZ and explored the effect of three SCZ-associated microcircuit alterations: reduced GABA activity, increased GABA decay times and NMDA receptor hypofunction. We investigated the effect of input strength on gamma (40 Hz) and beta (20 Hz) band power during gamma ASSR stimulation and saw that the pronounced increase in beta power during gamma stimulation seen experimentally could only be reproduced in the model when GABA decay times were increased and only for a specific range of input strengths. More specifically, when the input was in this specific range, the rhythmic drive at 40 Hz produced a strong 40 Hz rhythm in the control network; however, in the ‘SCZ-like’ network, the prolonged inhibition led to a so-called ‘beat-skipping’, where the network would only strongly respond to every other input. This mechanism was responsible for the emergence of the pronounced 20 Hz beta peak in the power spectrum. The other two microcircuit alterations were not able to produce a substantial 20 Hz component but they further narrowed the input strength range for which the network produced a beta component when combined with increased GABAergic decay times. Our finding that the beta component only existed for a specific range of input strengths might explain the seemingly inconsistent reporting in experimental studies and suggests that future ASSR studies should systematically explore different amplitudes of their stimuli. Furthermore, we provide a mechanistic link between a microcircuit alteration and an electrophysiological marker in schizophrenia and argue that more complex ASSR stimuli are needed to disentangle the nonlinear interactions of microcircuit alterations. The computational modelling approach put forward here is ideally suited to facilitate the development of such stimuli in a theory-based fashion.

Time-varying neurophysiological activity has been classically explored using correlation based sliding window analysis. However, this method employs only lower order statistics to track dynamic functional connectivity of the brain. We introduce recursive dynamic functional connectivity (rdFC) that incorporates higher order statistics to generate a multi-order connectivity pattern by analyzing neurophysiological data at multiple time scales. The technique builds a hierarchical graph between various temporal scales as opposed to traditional approaches that analyze each scale independently. We examined more than a million rdFC patterns obtained from morphologically diverse EEGs of 2378 subjects of varied age and neurological health. Spatiotemporal evaluation of these patterns revealed three dominant connectivity patterns that represent a universal underlying correlation structure seen across subjects and scalp locations. The three patterns are both mathematically equivalent and observed with equal prevalence in the data. The patterns were observed across a range of distances on the scalp indicating that they represent a spatially scale-invariant correlation structure. Moreover, the number of patterns representing the correlation structure has been shown to be linked with the number of nodes used to generate them. We also show evidence that temporal changes in the rdFC patterns are linked with seizure dynamics.

The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). MsCr measures reflect the behavior of conventional criticality measures (such as the branching parameter) across temporal scale. We applied MsCr to MEG and EEG data in several telling degraded information processing scenarios. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity: MsCr fingerprints showed deviance in the four states of compromised information processing examined in this study, disorders of consciousness, mild cognitive impairment, schizophrenia and even during pre-ictal activity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders., Aberrant synchronous oscillations have been associated with numerous brain disorders, including essential tremor. The authors show that synchronous cerebellar activity can casually affect essential tremor and that its underlying mechanism may be related to the temporal coherence of the tremulous movement.