scholarly journals Organizing principles of whole-brain functional connectivity in zebrafish larvae

2020 ◽  
Vol 4 (1) ◽  
pp. 234-256
Author(s):  
Richard F. Betzel
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
Brain Networks ◽  
Cellular Level ◽  
Geometric Constraints ◽  
Imaging Data ◽  
Whole Brain ◽  
Functional Connections ◽  
Low Dimensional ◽  
Organizing Principles

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.

scholarly journals Organizing principles of whole-brain functional connectivity in zebrafish larvae

2018 ◽  
Author(s):  
Richard Betzel
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
Brain Networks ◽  
Cellular Level ◽  
Geometric Constraints ◽  
Imaging Data ◽  
Whole Brain ◽  
Functional Connections ◽  
Low Dimensional ◽  
Organizing Principles

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal, for the first time, basic organizing principles of whole-brain functional brain networks at the microscale. Our overarching methodological framework provides a blueprint for studying the correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and microscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bo-yong Park ◽  
Jae-Joong Lee ◽  
Hong Ji Kim ◽  
Choong-Wan Woo ◽  
Hyunjin Park
Keyword(s):  
Back Pain ◽  
Pain Intensity ◽  
Large Scale ◽  
Brain Networks ◽  
Penalized Regression ◽  
Brain Network ◽  
Pain Modulation ◽  
Longitudinal Changes ◽  
Functional Connections ◽  
Intensity Changes

Abstract Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can serve as the markers is underexplored. Here, we aimed to identify a set of functional connections predictive of longitudinal changes in pain intensity using large-scale brain networks. We re-analyzed previously published resting-state functional magnetic resonance imaging data of 49 subacute back pain (SBP) patients. We built a network-level model that predicts changes in pain intensity over one year by combining independent component analysis and a penalized regression framework. Connections involving top-down pain modulation, multisensory integration, and mesocorticolimbic circuits were identified as predictive markers for pain intensity changes. Pearson’s correlations between actual and predicted pain scores were r = 0.33–0.72, and group classification results between SBP patients with persisting pain and recovering patients, in terms of area under the curve (AUC), were 0.89/0.75/0.75 for visits four/three/two, thus outperforming the previous work (AUC 0.83/0.73/0.67). This study identified functional connections important for longitudinal changes in pain intensity in SBP patients, providing provisional markers to predict future pain using large-scale brain networks.

scholarly journals Large-scale meta-analysis suggests low regional modularity in lateral frontal cortex

2016 ◽  
Author(s):  
Alejandro de la Vega ◽  
Tal Yarkoni ◽  
Tor D. Wager ◽  
Marie T. Banich
Keyword(s):  
Frontal Cortex ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Networks ◽  
Mental States ◽  
Whole Brain ◽  
Psychological States ◽  
Fmri Study ◽  
Data Driven Approach ◽  
Modes Of Processing

AbstractExtensive fMRI study of human lateral frontal cortex (LFC) has yet to yield a consensus mapping between discrete anatomy and psychological states, partly due to the difficulty of inferring mental states in individual studies. Here, we used a data-driven approach to generate a comprehensive functional-anatomical mapping of LFC from 11,406 neuroimaging studies. We identified putatively separable LFC regions on the basis of whole-brain co-activation, revealing 14 clusters organized into three whole-brain networks. Next, we used multivariate classification to identify the psychological states that best predicted activity in each sub-region, resulting in preferential psychological profiles. We observed large functional differences between networks, suggesting brain networks support distinct modes of processing. Within each network, however, we observed low functional specificity, suggesting discrete psychological states are not modularly organized. Our results are consistent with the view that individual LFC regions work as part of highly parallel, distributed networks to give rise to flexible, adaptive behavior.

scholarly journals Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

2019 ◽  
Author(s):  
Valeria Kebets ◽  
Avram J. Holmes ◽  
Csaba Orban ◽  
Siyi Tang ◽  
Jingwei Li ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Brain Networks ◽  
Higher Order ◽  
Large Set ◽  
Imaging Data ◽  
Multiple Control ◽  
Whole Brain ◽  
Diagnostic Categories ◽  
Intermediate Phenotypes ◽  
Latent Components

AbstractBackgroundThere is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thus neglecting the potential influence of huge swaths of the brain.MethodsA multivariate data-driven approach (partial least squares; PLS) was utilized to identify latent components linking a large set of clinical, cognitive and personality measures to whole-brain resting-state functional connectivity (RSFC) patterns across 224 participants. The participants were either healthy (N=110) or diagnosed with bipolar disorder (N=40), attention-deficit/hyperactivity disorder (N=37), schizophrenia (N=29) or schizoaffective disorder (N=8). In contrast to traditional case-control analyses, the diagnostic categories were not utilized in the PLS analysis, but were helpful for interpreting the components.ResultsOur analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction and impulsivity. Each component was associated with a unique whole-brain RSFC signature and shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network, and its connectivity with subcortical structures and cortical executive networks.ConclusionsWe identified three distinct dimensions with dissociable (but overlapping) whole-brain RSFC signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

scholarly journals The Global Signal and Observed Anticorrelated Resting State Brain Networks

2009 ◽  
Vol 101 (6) ◽  
pp. 3270-3283 ◽  
Author(s):  
Michael D. Fox ◽  
Dongyang Zhang ◽  
Abraham Z. Snyder ◽  
Marcus E. Raichle
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Great Promise ◽  
Blood Oxygen Level ◽  
Whole Brain ◽  
Global Signal ◽  
Global Regression

Resting state studies of spontaneous fluctuations in the functional MRI (fMRI) blood oxygen level dependent (BOLD) signal have shown great promise in mapping the brain's intrinsic, large-scale functional architecture. An important data preprocessing step used to enhance the quality of these observations has been removal of spontaneous BOLD fluctuations common to the whole brain (the so-called global signal). One reproducible consequence of global signal removal has been the finding that spontaneous BOLD fluctuations in the default mode network and an extended dorsal attention system are consistently anticorrelated, a relationship that these two systems exhibit during task performance. The dependence of these resting-state anticorrelations on global signal removal has raised important questions regarding the nature of the global signal, the validity of global signal removal, and the appropriate interpretation of observed anticorrelated brain networks. In this study, we investigate several properties of the global signal and find that it is, indeed, global, not residing preferentially in systems exhibiting anticorrelations. We detail the influence of global signal removal on resting state correlation maps both mathematically and empirically, showing an enhancement in detection of system-specific correlations and improvement in the correspondence between resting-state correlations and anatomy. Finally, we show that several characteristics of anticorrelated networks including their spatial distribution, cross-subject consistency, presence with modified whole brain masks, and existence before global regression are not attributable to global signal removal and therefore suggest a biological basis.

scholarly journals Multiple brain networks mediating stimulus-pain relationships in humans

2018 ◽  
Author(s):  
Stephan Geuter ◽  
Elizabeth A. Reynolds Losin ◽  
Mathieu Roy ◽  
Lauren Y. Atlas ◽  
Liane Schmidt ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Imaging Data ◽  
Pain Experience ◽  
Cognitive Components ◽  
Large Scale Analysis ◽  
Pain Ratings ◽  
Pain Experiences ◽  
Nociceptive Input

AbstractThe brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input, and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns mediating the relationship between stimulus intensity and pain. In a large-scale analysis of functional magnetic resonance imaging data (N=284), we identify both traditional mediators in somatosensory brain regions and additional mediators located in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole brain mediators are specific for pain vs. aversive sounds and are organized in five functional networks. Brain mediators explain 32% more within-subject variance of single-trial pain ratings than previous brain-based models. Our results provide a new, broader view of the networks underlying pain experience, as well as distinct targets for interventions.

scholarly journals Age-differences in information flow in executive and sensorimotor brain networks during childhood and adolescence

2020 ◽  
Author(s):  
Martina J. Lund ◽  
Dag Alnæs ◽  
Jaroslav Rokicki ◽  
Simon Schwab ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
Information Flow ◽  
Graphical Models ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Childhood And Adolescence ◽  
Imaging Data ◽  
Fine Grained ◽  
Age Related

AbstractObjectiveMental disorders often emerge during adolescence, and age-related differences in connection strengths of brain networks (static connectivity) have been identified. However, little is known about the directionality of information flow (directed connectivity) in this period of brain development.MethodsWe employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 979 participants aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on directionality.ResultsWe show robust bi-directionality in information flow between visual-medial and visual-lateral nodes of the network, in line with prior studies in adult samples. Furthermore, we found that age in this developmental sample was associated with directionality of information flow in sensorimotor and executive control networks, yet we did not find associations with cognitive abilities or psychopathology.DiscussionOur results revealed that directionality in information flow of large-scale brain networks is sensitive to age during adolescence, warranting further studies that may explore trajectories of development in more fine-grained network parcellations and in different populations.

scholarly journals Whole-brain estimates of directed connectivity for human connectomics

2020 ◽  
Author(s):  
Stefan Frässle ◽  
Zina M. Manjaly ◽  
Cao T. Do ◽  
Lars Kasper ◽  
Klaas P. Pruessmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Networks ◽  
Motor Task ◽  
Brain Network ◽  
Brain Organization ◽  
Whole Brain ◽  
Functional Roles ◽  
Reciprocal Connections ◽  
Theoretical Analyses

ABSTRACTConnectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation.Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

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