Graph analysis of EEG resting state functional networks in dyslexic readers

AbstractThe hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

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Alzheimer’s Disease Progressively Reduces Visual Functional Network Connectivity

Journal of Alzheimer s Disease Reports ◽

10.3233/adr-210017 ◽

2021 ◽

pp. 1-14

Author(s):

Jie Huang ◽

Paul Beach ◽

Andrea Bozoki ◽

David C. Zhu

Keyword(s):

Visual Cortex ◽

Lower Order ◽

Resting State ◽

Visual Processing ◽

Network Connectivity ◽

Higher Order ◽

Functional Network ◽

Functional Networks ◽

The Face ◽

Processing Network

Background: Postmortem studies of brains with Alzheimer’s disease (AD) not only find amyloid-beta (Aβ) and neurofibrillary tangles (NFT) in the visual cortex, but also reveal temporally sequential changes in AD pathology from higher-order association areas to lower-order areas and then primary visual area (V1) with disease progression. Objective: This study investigated the effect of AD severity on visual functional network. Methods: Eight severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a resting-state fMRI (rs-fMRI) and a task fMRI of viewing face photos. A resting-state visual functional connectivity (FC) network and a face-evoked visual-processing network were identified for each group. Results: For the HS, the identified group-mean face-evoked visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, the resting-state visual FC network was mainly confined within the visual cortex. AD disrupted these two functional networks in a similar severity dependent manner: the more severe the cognitive impairment, the greater reduction in network connectivity. For the face-evoked visual-processing network, MAD disrupted and reduced activation mainly in the higher-order visual association areas, with SAD further disrupting and reducing activation in the lower-order areas. Conclusion: These findings provide a functional corollary to the canonical view of the temporally sequential advancement of AD pathology through visual cortical areas. The association of the disruption of functional networks, especially the face-evoked visual-processing network, with AD severity suggests a potential predictor or biomarker of AD progression.

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Functional network topology of the right insula affects emotion dysregulation in hyperactive-impulsive attention-deficit/hyperactivity disorder

Scientific Reports ◽

10.1038/s41598-021-94426-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tammo Viering ◽

Pieter J. Hoekstra ◽

Alexandra Philipsen ◽

Jilly Naaijen ◽

Andrea Dietrich ◽

...

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Network Topology ◽

Resting State ◽

Latent Variable ◽

Emotion Dysregulation ◽

Adult Adhd ◽

Brain Regions ◽

Graph Analysis ◽

Hyperactivity Disorder ◽

The Right

AbstractEmotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in young adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficits in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroIMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and was associated with emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in young adult ADHD.

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Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽

10.1371/journal.pone.0082715 ◽

2013 ◽

Vol 8 (12) ◽

pp. e82715 ◽

Cited By ~ 29

Author(s):

Guihua Jiang ◽

Xue Wen ◽

Yingwei Qiu ◽

Ruibin Zhang ◽

Junjing Wang ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Functional Networks ◽

Whole Brain ◽

Brain Functional Networks ◽

Fmri Study ◽

Topological Organization

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Developmental changes in resting-state functional networks among individuals with and without internalizing psychopathologies

Depression and Anxiety ◽

10.1002/da.22864 ◽

2018 ◽

Vol 36 (2) ◽

pp. 141-152 ◽

Cited By ~ 6

Author(s):

Katie L. Burkhouse ◽

Jonathan P. Stange ◽

Rachel H. Jacobs ◽

Runa Bhaumik ◽

Katie L. Bessette ◽

...

Keyword(s):

Resting State ◽

Developmental Changes ◽

Functional Networks

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Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2020.599112 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yifei Zhang ◽

Xiaodan Chen ◽

Xinyuan Liang ◽

Zhijiang Wang ◽

Teng Xie ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Human Brain ◽

Cognitive Performance ◽

Power Law ◽

Resting State ◽

Brain Networks ◽

Functional Networks ◽

Whole Brain ◽

Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

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