Heterogeneity of EEG resting-state brain networks in absolute pitch

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.

Download Full-text

Characterizations of resting-state modulatory interactions in the human brain

Journal of Neurophysiology ◽

10.1152/jn.00893.2014 ◽

2015 ◽

Vol 114 (5) ◽

pp. 2785-2796 ◽

Cited By ~ 22

Author(s):

Xin Di (邸新) ◽

Bharat B. Biswal

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Interaction Analysis ◽

Region Of Interest ◽

Resting State Fmri ◽

Brain Regions ◽

Processing Efficiency ◽

Whole Brain ◽

Positive Effects

Functional connectivity between two brain regions, measured using functional MRI (fMRI), has been shown to be modulated by other regions even in a resting state, i.e., without performing specific tasks. We aimed to characterize large-scale modulatory interactions by performing region-of-interest (ROI)-based physiophysiological interaction analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all of the significant modulatory interactions, there were considerably more negative than positive effects; i.e., in more cases, an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as to whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases in which the three ROIs were from a single module, suggesting an increase within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases in which the three ROIs were from two modules, suggesting a tendency of between-module segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole-brain characteristics of modulatory interactions and may provide guidance for future studies of connectivity dynamics in both resting state and task state.

Download Full-text

Neuronal cascades shape whole-brain functional dynamics at rest

10.1101/2020.12.25.424385 ◽

2020 ◽

Author(s):

Giovanni Rabuffo ◽

Jan Fousek ◽

Christophe Bernard ◽

Viktor Jirsa

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Blood Oxygen Level Dependent ◽

Brain Network ◽

Neural Activation ◽

Data Set ◽

Whole Brain ◽

Dynamic Component ◽

Leading Role ◽

Co Activation

AbstractAt rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

Download Full-text

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

eLife ◽

10.7554/elife.32696 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 4

Author(s):

Ruedeerat Keerativittayayut ◽

Ryuta Aoki ◽

Mitra Taghizadeh Sarabi ◽

Koji Jimura ◽

Kiyoshi Nakahara

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Large Scale ◽

Brain Regions ◽

Brain Network ◽

Time Varying ◽

Memory Encoding ◽

Graph Metrics ◽

Connectivity Patterns ◽

Network States

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.

Download Full-text

Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

10.1101/556555 ◽

2019 ◽

Cited By ~ 2

Author(s):

Narges Moradi ◽

Mehdy Dousty ◽

Roberto C. Sotero

Keyword(s):

Functional Connectivity ◽

Empirical Mode Decomposition ◽

Resting State ◽

Large Scale ◽

Pearson Correlation ◽

Brain Regions ◽

Connectivity Matrix ◽

Correlation Pattern ◽

Mode Decomposition ◽

Global Signal

AbstractResting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

Download Full-text

Brain Resting State Functional Networks in Infants with Prenatal Opioid Exposure

Proceedings of IMPRS ◽

10.18060/25736 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Nicole Steinhardt ◽

Ramana Vishnubhotla ◽

Yi Zhao ◽

David M. Haas ◽

Gregory M. Sokol ◽

...

Keyword(s):

Functional Connectivity ◽

Functional Mri ◽

Gestational Age ◽

Resting State ◽

Brain Regions ◽

Brain Network ◽

Anterior Cingulate ◽

Global Network ◽

Neurodevelopmental Outcomes ◽

Distinct Component

Purpose: Infants of mothers with opioid and substance use can present with postnatal withdrawal symptoms and are at risk of poor neurodevelopmental outcomes in later childhood. Identifying methods to evaluate the consequences of substance exposure on the developing brain can help initiate proactive therapies to improve outcomes for opioid-exposed neonates. Additionally, early brain imaging in infancy has the potential to identify early brain developmental alterations that could prognosticate neurodevelopmental outcomes in these children. In this study, we aim to identify differences in global brain network connectivity in infants with prenatal opioid exposure compared to healthy control infants, using resting-state functional MRI performed at less than 2 months completed gestational age. Materials and Methods: In this prospective, IRB-approved study, we recruited 20 infants with prenatal opioid exposure and 20 healthy, opioid naïve infants. Anatomic imaging and resting-state functional MRI were performed at less than 48 weeks corrected gestational age, and rs-fMRI images were co-registered to the UNC neonate brain template and 90 anatomic atlas-labelled regions. Covariate Assisted Principal (CAP) regression was performed to identify brain network functional connectivity that was significantly different among infants with prenatal opioid exposure compared to healthy neonates. Results: Of the 5 significantly different CAP components identified, the most distinct component (CAP5, p= 3.86 x 10-6) spanned several brain regions, including the right inferior temporal gyrus, bilateral Hesch’s gyrus, left thalamus, left supramarginal gyrus, left inferior parietal lobule, left superior parietal gyrus, right anterior cingulate gyrus, right gyrus rectus, left supplementary motor area, and left pars triangularis. Functional connectivity in this network was lower in the infants with prenatal opioid exposure compared to non-opioid exposed infants. Conclusion: This study demonstrates global network alterations in infants with prenatal opioid exposure compared to non-opioid exposed infants. Future studies should be aimed at identifying clinical significance of this altered connectivity.

Download Full-text

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.

Download Full-text

The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity

10.1101/2020.01.20.912881 ◽

2020 ◽

Author(s):

Michael Eyre ◽

Sean P Fitzgibbon ◽

Judit Ciarrusta ◽

Lucilio Cordero-Grande ◽

Anthony N Price ◽

...

Keyword(s):

Preterm Birth ◽

Functional Connectivity ◽

Large Scale ◽

Developmental Coordination Disorder ◽

Positive Association ◽

Brain Regions ◽

Higher Order ◽

Dependent Manner ◽

Normal Term ◽

Human Connectome Project

AbstractThe Developing Human Connectome Project (dHCP) is an Open Science project which provides the first large sample of neonatal functional MRI (fMRI) data with high temporal and spatial resolution. This data enables mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organisation in humans. Here, we characterise in unprecedented detail the maturation and integrity of resting-state networks (RSNs) at normal term age in 337 infants (including 65 born preterm).First, we applied group independent component analysis (ICA) to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder which occur frequently in preterm children.Overall, we find a robust, modular, symmetrical functional brain organisation at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher-order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

Download Full-text

Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

Scientific Reports ◽

10.1038/s41598-020-77137-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Reema Shafi ◽

Adrian P. Crawley ◽

Maria Carmela Tartaglia ◽

Charles H. Tator ◽

Robin E. Green ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Brain Regions ◽

Healthy Controls ◽

Postconcussion Syndrome ◽

Resting State Functional Connectivity ◽

Post Injury ◽

Network Nodes ◽

Specific Impact

AbstractConcussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

Download Full-text

Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

Network Neuroscience ◽

10.1162/netn_a_00129 ◽

2020 ◽

Vol 4 (2) ◽

pp. 448-466

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Resting State ◽

Large Scale ◽

Brain Network ◽

Generative Models ◽

Network Activity ◽

Fmri Data ◽

State Transitions ◽

Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

Download Full-text

Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence

10.31234/osf.io/tzd3w ◽

2021 ◽

Author(s):

Roberto A. Abreu-Mendoza ◽

Melanie Pincus ◽

Yaira Chamorro ◽

Dietsje Jolles ◽

Esmeralda Matute ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Math Achievement ◽

Resting State Fmri ◽

Brain Regions ◽

Math Performance ◽

Resting State Functional Connectivity ◽

Neural Basis ◽

Math Ability ◽

Control Seed

Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age=16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporooccipital regions, were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as connectivity of Heschl’s gyrus, a control seed not involved in math cognition, was not modulated by math performance. Together, our results point to adolescence as a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability in this period.

Download Full-text