Scale-resolved analysis of brain functional connectivity networks with spectral entropy

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

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Neurocognitive Aging and Functional Connectivity Using Functional Magnetic Resonance Imaging

Oxford Research Encyclopedia of Psychology ◽

10.1093/acrefore/9780190236557.013.385 ◽

2018 ◽

Author(s):

Hana Burianová

Keyword(s):

Functional Connectivity ◽

Cognitive Processes ◽

Large Scale ◽

Healthy Aging ◽

Brain Activity ◽

Brain Regions ◽

Effective Strategies ◽

Cognitive Improvement ◽

Age Related ◽

Neurocognitive Aging

Determining the mechanisms that underlie neurocognitive aging, such as compensation or dedifferentiation, and facilitating the development of effective strategies for cognitive improvement is essential due to the steadily rising aging population. One approach to study the characteristics of healthy aging comprises the assessment of functional connectivity, delineating markers of age-related neurocognitive plasticity. Functional connectivity paradigms characterize complex one-to-many (or many-to-many) structure–function relations, as higher-level cognitive processes are mediated by the interaction among a number of functionally related neural areas rather than localized to discrete brain regions. Task-related or resting-state interregional correlations of brain activity have been used as reliable indices of functional connectivity, delineating age-related alterations in a number of large-scale brain networks, which subserve attention, working memory, episodic retrieval, and task-switching. Together with behavioral and regional activation studies, connectivity studies and modeling approaches have contributed to our understanding of the mechanisms of age-related reorganization of distributed functional networks; specifically, reduced neural specificity (dedifferentiation) and associated impairment in inhibitory control and compensatory neural recruitment.

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Reproducible functional connectivity alterations are associated with autism spectrum disorder

10.1101/303115 ◽

2018 ◽

Author(s):

Štefan Holiga ◽

Joerg F. Hipp ◽

Christopher H. Chatham ◽

Pilar Garces ◽

Will Spooren ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Large Scale ◽

Clinical Symptoms ◽

Brain Activity ◽

Network Connectivity ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Replication Procedure ◽

Medication Status

AbstractDespite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.

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Inference of Synaptic Connectivity and External Variability in Neural Microcircuits

10.1101/650069 ◽

2019 ◽

Author(s):

Cody Baker ◽

Emmanouil Froudarakis ◽

Dimitri Yatsenko ◽

Andreas S. Tolias ◽

Robert Rosenbaum

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Network Models ◽

Ground Truth ◽

External Input ◽

Synaptic Connectivity ◽

Imaging Data ◽

Common Input ◽

Simplifying Assumptions

AbstractA major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is often closely related to synaptic connectivity in practice under various network models. This relation becomes more pronounced when the spatial structure of neuronal variability is considered jointly with precision.

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Functional brain network reconfiguration during learning in a dynamic environment

10.1101/800284 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chang-Hao Kao ◽

Ankit N. Khambhati ◽

Danielle S. Bassett ◽

Matthew R. Nassar ◽

Joseph T. McGuire ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Functional Integration ◽

Dynamic Environment ◽

Brain Network ◽

Uncertain Environments ◽

Whole Brain ◽

Functional Brain Network ◽

Brain Pattern ◽

Brain Functional Connectivity

AbstractWhen learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

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Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

10.1101/2020.03.26.009795 ◽

2020 ◽

Author(s):

Paul Triebkorn ◽

Joelle Zimmermann ◽

Leon Stefanovski ◽

Dipanjan Roy ◽

Ana Solodkin ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Function ◽

Large Scale ◽

Brain Activity ◽

Brain Network ◽

Model Parameters ◽

Network Modelling ◽

Neural Populations ◽

Global Coupling ◽

The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

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Altered Large-Scale Brain Functional Connectivity in Ocular Hypertension

Frontiers in Neuroscience ◽

10.3389/fnins.2020.00146 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 4

Author(s):

Antonio Giorgio ◽

Jian Zhang ◽

Francesco Costantino ◽

Nicola De Stefano ◽

Paolo Frezzotti

Keyword(s):

Functional Connectivity ◽

Ocular Hypertension ◽

Large Scale ◽

Brain Functional Connectivity

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Disrupted architecture of large-scale brain functional connectivity networks in patients with generalized tonic–clonic seizure

Applied Informatics ◽

10.1186/s40535-017-0045-2 ◽

2017 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Rong Li ◽

Yangyang Yu ◽

Wei Liao ◽

Zhiqiang Zhang ◽

Guangming Lu ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Clonic Seizure ◽

Tonic Clonic Seizure ◽

Brain Functional Connectivity

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Ongoing dynamics in large-scale functional connectivity predict perception

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1420687112 ◽

2015 ◽

Vol 112 (27) ◽

pp. 8463-8468 ◽

Cited By ~ 138

Author(s):

Sepideh Sadaghiani ◽

Jean-Baptiste Poline ◽

Andreas Kleinschmidt ◽

Mark D’Esposito

Keyword(s):

Functional Connectivity ◽

Network Structure ◽

Large Scale ◽

Brain Activity ◽

Network Connectivity ◽

Brain Regions ◽

Modular Network ◽

Auditory Detection ◽

Correlation Strength ◽

The Difference

Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22–40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency.

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