Reduced Functional Connectivity in Children With Congenital Cataracts Using Resting-State Electroencephalography Measurement

ObjectivesNumerous task-based functional magnetic resonance imaging studies indicate the presence of compensatory functional improvement in patients with congenital cataracts. However, there is neuroimaging evidence that shows decreased sensory perception or cognition information processing related to visual dysfunction, which favors a general loss hypothesis. This study explored the functional connectivity between visual and other networks in children with congenital cataracts using resting state electroencephalography.MethodsTwenty-one children with congenital cataracts (age: 8.02 ± 2.03 years) and thirty-five sex- and age-matched normal sighted controls were enrolled to investigate functional connectivity between the visual cortex and the default mode network, the salience network, and the cerebellum network during resting state electroencephalography (eyes closed) recordings.ResultThe congenital cataract group was less active, than the control group, in the occipital, temporal, frontal and limbic lobes in the theta, alpha, beta1 and beta2 frequency bands. Additionally, there was reduced alpha-band connectivity between the visual and somatosensory cortices and between regions of the frontal and parietal cortices associated with cognitive and attentive control.ConclusionThe results indicate abnormalities in sensory, cognition, motion and execution functional connectivity across the developing brains of children with congenital cataracts when compared with normal controls. Reduced frontal alpha activity and alpha-band connectivity between the visual cortex and salience network might reflect attenuated inhibitory information flow, leading to higher attentional states, which could contribute to adaptation of environmental change in this group of patients.

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EEG resting-state functional connectivity: evidence for an imbalance of external/ internal information integration in Autism

10.21203/rs.3.rs-1107850/v1 ◽

2021 ◽

Author(s):

Prany Wantzen ◽

Patrice Clochon ◽

Franck Doidy ◽

Fabrice Wallois ◽

Mahdi Mahmoudzadeh ◽

...

Keyword(s):

Functional Connectivity ◽

Information Integration ◽

Resting State ◽

Phase Synchronization ◽

Current Source ◽

Autism Spectrum ◽

High Temporal Resolution ◽

Alpha Band ◽

Eyes Closed ◽

Internal Information

Abstract Background Autism spectrum disorder (ASD) is associated with atypical neural activity in resting-state. Most of the studies have focused on abnormalities in alpha-frequency, as a marker of ASD dysfunctions. However, few have explored alpha synchronization, with a specific interest in resting-state networks: the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD. Methods Using the high temporal resolution of EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) in 29 participants with ASD and 38 age,- sex- and IQ-matched typically developing (TD) controls. Results We observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band. Conclusion These results may account for impairments in multimodal - sensory and internal information - integration frequently observed in ASD. Underconnectivity potentially involves difficulties switching between this externally oriented attention and internally oriented thoughts and, more broadly, may impact embodied cognition.

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Resting-State Alpha-Band Oscillations in Migraine

Perception ◽

10.1177/0301006618755639 ◽

2018 ◽

Vol 47 (4) ◽

pp. 379-396 ◽

Cited By ~ 5

Author(s):

Louise O’Hare ◽

Federica Menchinelli ◽

Simon J. Durrant

Keyword(s):

Resting State ◽

Multiplicative Noise ◽

Temporal Integration ◽

Internal Noise ◽

Control Group ◽

Alpha Band ◽

Control Groups ◽

Eyes Closed ◽

Before And After ◽

And Control

Migraine groups show differences in motion perception compared with controls, when tested in between migraine attacks (interictally). This is thought to be due to an increased susceptibility to stimulus degradation (multiplicative internal noise). Fluctuations in alpha-band oscillations are thought to regulate visual perception, and so differences could provide a mechanism for the increased multiplicative noise seen in migraine. The aim of this article was to characterise resting-state alpha-band oscillations (between 8 and 12 Hz) in the visual areas of the brain in migraine and control groups. Alpha-band activity in the resting state (with eyes closed) was recorded before and after a visual psychophysics task to estimate equivalent noise, specifically a contrast detection task. The lower alpha-band (8 to 10 Hz) resting-state alpha-band power was increased in the migraine compared with the control group, which may provide a mechanism for increased multiplicative noise. In agreement with previous research, there were no differences found in the additive (baseline) internal noise, estimated using an equivalent noise task in the same observers. As fluctuations in alpha-band oscillations control the timing of perceptual processing, increased lower alpha-band (8 to 10 Hz) power could explain the behavioural differences in migraine compared with control groups, particularly on tasks relying on temporal integration.

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How reliable are the functional connectivity networks of MEG in resting states?

Journal of Neurophysiology ◽

10.1152/jn.00335.2011 ◽

2011 ◽

Vol 106 (6) ◽

pp. 2888-2895 ◽

Cited By ~ 39

Author(s):

Seung-Hyun Jin ◽

Jaeho Seol ◽

June Sic Kim ◽

Chun Kee Chung

Keyword(s):

Functional Connectivity ◽

Resting State ◽

High Reliability ◽

Intraclass Correlation ◽

Alpha Band ◽

Eyes Closed ◽

Gamma Frequency ◽

Centrality Metrics ◽

Eyes Open ◽

Network Metrics

We investigated the reliability of nodal network metrics of functional connectivity (FC) networks of magnetoencephalography (MEG) covering the whole brain at the sensor level in the eyes-closed (EC) and eyes-open (EO) resting states. Mutual information (MI) was employed as a measure of FC between sensors in theta, alpha, beta, and gamma frequency bands of MEG signals. MI matrices were assessed with three nodal network metrics, i.e., nodal degree (Dnodal), nodal efficiency (Enodal), and betweenness centrality (normBC). Intraclass correlation (ICC) values were calculated as a measure of reliability. We observed that the test-retest reliabilities of the resting states ranged from a poor to good level depending on the bands and metrics used for defining the nodal centrality. The dominant alpha-band FC network changes were the salient features of the state-related FC changes. The FC networks in the EO resting state showed greater reliability when assessed by Dnodal (maximum mean ICC = 0.655) and Enodal (maximum mean ICC = 0.604) metrics. The gamma-band FC network was less reliable than the theta, alpha, and beta networks across the nodal network metrics. However, the sensor-wise ICC values for the nodal centrality metrics were not uniformly distributed, that is, some sensors had high reliability. This study provides a sense of how the nodal centralities of the human resting state MEG are distributed at the sensor level and how reliable they are. It also provides a fundamental scientific background for continued examination of the resting state of human MEG.

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Functional connectivity between the thalamus and visual cortex under eyes closed and eyes open conditions: A resting-state fMRI study

Human Brain Mapping ◽

10.1002/hbm.20728 ◽

2009 ◽

Vol 30 (9) ◽

pp. 3066-3078 ◽

Cited By ~ 104

Author(s):

Qihong Zou ◽

Xiangyu Long ◽

Xinian Zuo ◽

Chaogan Yan ◽

Chaozhe Zhu ◽

...

Keyword(s):

Visual Cortex ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Eyes Closed ◽

Fmri Study ◽

Eyes Open

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The effect of congenital blindness on resting-state functional connectivity revisited

Scientific Reports ◽

10.1038/s41598-021-91976-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maria J. S. Guerreiro ◽

Madita Linke ◽

Sunitha Lingareddy ◽

Ramesh Kekunnaya ◽

Brigitte Röder

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Visual Experience ◽

Brain Regions ◽

State Condition ◽

Resting State Functional Connectivity ◽

Congenital Blindness ◽

Eyes Closed ◽

Congenitally Blind ◽

Eyes Open

AbstractLower resting-state functional connectivity (RSFC) between ‘visual’ and non-‘visual’ neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition—as well as to evaluate the effect of resting state condition on group differences in RSFC—, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between ‘visual’ and non-‘visual’ circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.

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Motor Sequences - Separating The Sequence From The Motor. A longitudinal rsfMRI Study

10.1101/2021.02.09.430495 ◽

2021 ◽

Author(s):

ATP Jäger ◽

JM Huntenburg ◽

SA Tremblay ◽

U Schneider ◽

S Grahl ◽

...

Keyword(s):

Functional Connectivity ◽

Motor Learning ◽

Resting State ◽

Primary Motor Cortex ◽

Control Group ◽

Motor Execution ◽

Motor Sequence ◽

Resting State Functional Connectivity ◽

Fast Learning ◽

Slow Learning

AbstractIn motor learning, sequence-specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific increases in functional connectivity during fast learning in the sensorimotor territory of the internal segment of right globus pallidus (GPi), and sequence-specific decreases in right supplementary motor area (SMA) in overall learning. We found that connectivity changes in key regions of the motor network including the superior parietal cortex (SPC) and primary motor cortex (M1) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA and GPi that has previously been identified in online task-based learning studies in humans and primates, and extends it to resting state network changes after sequence-specific MSL. Finally, our results shed light on a timing-specific plasticity mechanism between GPi and SMA following MSL.

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Macroscopic cortical dynamics: Spatially uncorrelated but temporally coherent rich-club organisations in source-space resting-state EEG

10.1101/2020.07.23.217786 ◽

2020 ◽

Author(s):

Steve Mehrkanoon

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Complex Dynamics ◽

Cortical Activity ◽

Computational Technique ◽

Salience Network ◽

Resting State Functional Connectivity ◽

Default Mode ◽

Rich Club ◽

Topological Features

AbstractSynchronous oscillations of neuronal populations support resting-state cortical activity. Recent studies indicate that resting-state functional connectivity is not static, but exhibits complex dynamics. The mechanisms underlying the complex dynamics of cortical activity have not been well characterised. Here, we directly apply singular value decomposition (SVD) in source-reconstructed electroencephalography (EEG) in order to characterise the dynamics of spatiotemporal patterns of resting-state functional connectivity. We found that changes in resting-state functional connectivity were associated with distinct complex topological features, “Rich-Club organisation”, of the default mode network, salience network, and motor network. Rich-club topology of the salience network revealed greater functional connectivity between ventrolateral prefrontal cortex and anterior insula, whereas Rich-club topologies of the default mode networks revealed bilateral functional connectivity between fronto-parietal and posterior cortices. Spectral analysis of the dynamics underlying Rich-club organisations of these source-space network patterns revealed that resting-state cortical activity exhibit distinct dynamical regimes whose intrinsic expressions contain fast oscillations in the alpha-beta band and with the envelope-signal in the timescale of < 0.1 Hz. Our findings thus demonstrated that multivariate eigen-decomposition of source-reconstructed EEG is a reliable computational technique to explore how dynamics of spatiotemporal features of the resting-state cortical activity occur that oscillate at distinct frequencies.

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Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

Advances in Computational Intelligence and Robotics - Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence ◽

10.4018/978-1-7998-3038-2.ch009 ◽

2020 ◽

pp. 169-186

Author(s):

S. Vidhusha ◽

A. Kavitha

Keyword(s):

Functional Connectivity ◽

Granger Causality ◽

Resting State ◽

Cognitive Task ◽

Resting State Fmri ◽

Autism Spectrum ◽

Control Group ◽

Neural Connectivity ◽

Activation Patterns ◽

The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

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