Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

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Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/451516 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 22

Author(s):

Ali Yener Mutlu ◽

Edward Bernat ◽

Selin Aviyente

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Dynamic Network ◽

Principal Component ◽

Brain Network ◽

Event Related Potential ◽

Time Varying ◽

Error Related Negativity ◽

Connectivity Patterns

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

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Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Brain Sciences ◽

10.3390/brainsci10110777 ◽

2020 ◽

Vol 10 (11) ◽

pp. 777

Author(s):

Nicholas John Simos ◽

Stavros I. Dimitriadis ◽

Eleftherios Kavroulakis ◽

Georgios C. Manikis ◽

George Bertsias ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Lupus Erythematosus ◽

Statistical Power ◽

Functional Neuroimaging ◽

Brain Regions ◽

Brain Network ◽

Functional Brain ◽

Graph Metrics ◽

Functional Brain Network

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

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Behavioural relevance of spontaneous, transient brain network interactions in fMRI

10.1101/779736 ◽

2019 ◽

Cited By ~ 2

Author(s):

D. Vidaurre ◽

A. Llera ◽

S.M. Smith ◽

M.W. Woolrich

Keyword(s):

Functional Connectivity ◽

Network Architecture ◽

Brain Networks ◽

Structural Data ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Human Connectome Project ◽

Brain Data ◽

Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

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Assessing HD-EEG functional connectivity states using a human brain computational model

10.1101/2021.10.14.464481 ◽

2021 ◽

Author(s):

Judie Tabbal ◽

Aya Kabbara ◽

Maxime Yochum ◽

Mohamad Khalil ◽

Mahmoud HASSAN ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Computational Model ◽

Diffusion Tensor ◽

Clustering Algorithms ◽

Structural Connectivity ◽

Brain Network ◽

Systematic Evaluation ◽

Fast Time ◽

Network States

Electro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool to track fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of the various steps, from source localization and functional connectivity to clustering algorithms, is challenging, due to the lack of realistic controlled data. Here, we used a human brain computational model containing both physiologically-based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging -based structural connectivity, to generate realistic High Density-EEG (256 channels) recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas in order to emulate a virtual picture naming task. We identified the fast time-varying network states and quantified the performance of the key steps involved in the pipeline: (1) inverse models to reconstruct cortical-level sources, (2) functional connectivity measures to compute statistical interdependency between regional time series, and (3) dimensionality reduction methods to derive dominant brain network states (BNS). Using a systematic evaluation of the different independent/principal/non-negative decomposition techniques along with a clustering approach, results show significant variability among the tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, as well as the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. Our aim here is twofold: 1) to provide quantitative assessment on the advantages and the limitations of each of the analyzed techniques and 2) to introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. With such framework, other tasks can be generated and used for validation and other methodological points can be also addressed.

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Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity

Network Neuroscience ◽

10.1162/netn_a_00037 ◽

2018 ◽

Vol 2 (4) ◽

pp. 397-417 ◽

Cited By ~ 36

Author(s):

Aaron Kucyi ◽

Arielle Tambini ◽

Sepideh Sadaghiani ◽

Shella Keilholz ◽

Jessica R. Cohen

Keyword(s):

Functional Connectivity ◽

Cognitive Processes ◽

Functional Neuroimaging ◽

Brain Connectivity ◽

Network Dynamics ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Correlated Activity ◽

External Stimuli

In cognitive neuroscience, focus is commonly placed on associating brain function with changes in objectively measured external stimuli or with actively generated cognitive processes. In everyday life, however, many forms of cognitive processes are initiated spontaneously, without an individual’s active effort and without explicit manipulation of behavioral state. Recently, there has been increased emphasis, especially in functional neuroimaging research, on spontaneous correlated activity among spatially segregated brain regions (intrinsic functional connectivity) and, more specifically, on intraindividual fluctuations of such correlated activity on various time scales (time-varying functional connectivity). In this Perspective, we propose that certain subtypes of spontaneous cognitive processes are detectable in time-varying functional connectivity measurements. We define these subtypes of spontaneous cognitive processes and review evidence of their representations in time-varying functional connectivity from studies of attentional fluctuations, memory reactivation, and effects of baseline states on subsequent perception. Moreover, we describe how these studies are critical to validating the use of neuroimaging tools (e.g., fMRI) for assessing ongoing brain network dynamics. We conclude that continued investigation of the behavioral relevance of time-varying functional connectivity will be beneficial both in the development of comprehensive neural models of cognition, and in informing on best practices for studying brain network dynamics.

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Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth

Translational Psychiatry ◽

10.1038/s41398-021-01704-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chandra Sripada ◽

Mike Angstadt ◽

Aman Taxali ◽

D. Angus Clark ◽

Tristan Greathouse ◽

...

Keyword(s):

Socioeconomic Status ◽

Cognitive Development ◽

Functional Connectivity ◽

Cognitive Ability ◽

Large Scale ◽

Strong Predictor ◽

Brain Networks ◽

Brain Regions ◽

General Cognitive Ability ◽

Connectivity Patterns

AbstractGeneral cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η2). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10−16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.

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Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

10.1101/519868 ◽

2019 ◽

Cited By ~ 1

Author(s):

Heather Shappell ◽

Brian S. Caffo ◽

James J. Pekar ◽

Martin A. Lindquist

Keyword(s):

Functional Connectivity ◽

Markov Models ◽

Brain Networks ◽

Resting State Fmri ◽

Brain Regions ◽

Fmri Data ◽

Time Varying ◽

Human Connectome Project ◽

Functional Brain Networks ◽

Network States

AbstractThe study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject’s most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.

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

Scientific Reports ◽

10.1038/s41598-020-78899-7 ◽

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.

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