Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

Khan, Sheraz; Hashmi, Javeria A.; Mamashli, Fahimeh; Michmizos, Konstantinos P.; Kitzbichler, Manfred G.; Bharadwaj, Hari; Bekhti, Yousra; Ganesan, Santosh; Garel, Keri-Lee A.; Whitfield-Gabrieli, Susan; Gollub, Randy L.; Kong, Jian; Vaina, Lucia M.; Rana, Kunjan D.; Stufflebeam, Steven M.; Hämäläinen, Matti S.; Kenet, Tal

doi:10.1016/j.neuroimage.2018.02.018

Cited by 59 publications

(49 citation statements)

References 98 publications

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“…This may distort resulting time-courses even if only one single source is captured by the ROI. In practice, ROIs are therefore often selected according to specific data analysis goals ( Farahibozorg et al., 2018 ; Colclough et al., 2016 ; Khan et al., 2018 ) as is generally recommended for many elements of MEG and EEG analysis ( Jas et al., 2018 ). In light of these considerations, we chose the rather coarse Desikan-Killiany parcellation ( Desikan et al., 2006 ) from FreeSurfer that covers both hemispheres on each individual cortex with 28 ROIs.…”

Section: Methodsmentioning

confidence: 99%

Emergence of β and γ networks following multisensory training

et al. 2020

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Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent unisensory perceptual task. For this, we investigated oscillatory activity and functional connectivity as a function of individuals’ sensory history during training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography. Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional interactions as a function of sensory history. One major finding is the emergence of a large-scale synchronization in the high γ (gamma: ) and β (beta: ) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes.

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Section: Methodsmentioning

confidence: 99%

Emergence of β and γ networks following multisensory training

et al. 2020

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“…In fact, although the network consisted of the same regions, these were sparsely or weakly connected in children as compared to adults. Further, Khan et al (2018) reported that the DMN continues to mature between at least 8 years old and adulthood on a frequency-band-dependent fashion. Different sets of regions showed changes in different frequency bands, and they showed different temporal patterns.…”

Section: Functional Connectivity Resultsmentioning

confidence: 99%

I know that I don’t know: Structural and functional connectivity underlying meta-ignorance in pre-schoolers

Filevich

Forlim

Fehrman

et al. 2018

Preprint

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Research Highlights[1] Children develop the ability to report that they do not know something at around five years of age.[2] Children who could correctly report their own ignorance in a partialknowledge task showed thicker cortices within medial orbitofrontal cortex.[3] This region was functionally connected to parts of the default-mode network.[4] The default-mode network might support the development of correct metacognitive monitoring. AbstractMetacognition plays a pivotal role in human development. The ability to realize that we do not know something, or meta-ignorance, emerges after approximately five years of age. We aimed at identifying the brain systems that underlie the developmental emergence of this ability in a preschool sample.Twenty-four children aged between five and six years answered questions under three conditions of a meta-ignorance task twice. In the critical partial knowledge condition, an experimenter first showed two toys to a child, then announced that she would place one of them in a box behind a screen, out of sight from the child.The experimenter then asked the child whether or not she knew which toy was in the box.Children who answered correctly both times to the metacognitive question in the partial knowledge condition (n=9) showed greater cortical thickness in a cluster within left medial orbitofrontal cortex than children who did not (n=15).Further, seed-based functional connectivity analyses of the brain during resting state revealed that this region is functionally connected to the medial orbitofrontal gyrus, posterior cingulate gyrus and precuneus, and mid-and inferior temporal gyri.This finding suggests that the default mode network, critically through its prefrontal regions, supports introspective processing. It leads to the emergence 4 of metacognitive monitoring allowing children to explicitly report their own ignorance.

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“…As shown in Figure 1C, we applied the orthogonal correlations to compute the connectivity metrics involved in handling source leakages using the "envelope_correlation" function in MNE (17,20) at delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-20 Hz), high beta (20-30 Hz), and gamma (30-80 Hz) bands (17). The "envelope_correlation" obtains the correlation values according to the following calculation flow.…”

Section: Data Analysis Procedures For Resting-state Networkmentioning

confidence: 99%

“…In recent years, however, two British groups have developed methodological approaches to evaluate the frequency-specific resting-state connectivity using MEG ( 17 , 18 ). These advances have enabled the investigation of frequency-specific alterations of resting-state networks via graph theory in autism ( 19 ) and age-related changes in these networks ( 20 ).…”

Section: Introductionmentioning

confidence: 99%

Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study

et al. 2020

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Background: Bipolar disorder (BD) is a serious psychiatric disorder that is associated with a high suicide rate, and for which no clinical biomarker has yet been identified. To address this issue, we investigated the use of magnetoencephalography (MEG) as a new prospective tool. MEG has been used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequencyspecific resting-state connectome in patients with BD. This resting-state MEG study explored the oscillatory representations of clinical symptoms of BD via graph analysis. Methods: In this prospective case-control study, 17 patients with BD and 22 healthy controls (HCs) underwent resting-state MEG and evaluations for depressive and manic symptoms. After estimating the source current distribution, orthogonalized envelope correlations between multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into seven left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare the intra-and inter-community edges between the two groups. Results: In the BD group, we found significantly increased inter-community edges of the right LM-right DMN at the gamma band, and decreased inter-community edges of the right SAL-right FPN at the delta band and the left SAL-right SAL at the theta band. Intracommunity edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group. The number of connections in the left LM at the high beta band showed positive correlations with the subjective and objective depressive symptoms in the BD group. Conclusion: We introduced graph theory into resting-state MEG studies to investigate the functional connectivity in patients with BD. To the best of our knowledge, this is a novel approach that may be beneficial in the diagnosis of BD. This study describes the

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Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

Cited by 59 publications

References 98 publications

Emergence of β and γ networks following multisensory training

Emergence of β and γ networks following multisensory training

I know that I don’t know: Structural and functional connectivity underlying meta-ignorance in pre-schoolers

Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study

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