Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks

Barber, Anita D.; Hegarty, Catherine E.; Lindquist, Martin A.; Karlsgodt, Katherine H.

doi:10.1093/cercor/bhaa391

Cited by 32 publications

(27 citation statements)

References 86 publications

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“…The heritability of functional connectivity (commonly quantified as the correlation of activity fluctuations between pairs of regions), has been studied using static (Colclough et al, 2017;Fornito et al, 2011;Fu et al, 2015;Glahn et al, 2010;Sinclair et al, 2015;Sudre et al, 2017) and dynamic fMRI (Barber et al, 2021), MEG (Colclough et al, 2017), and EEG (Schutte et al, 2013;Smit et al, 2008;van den Heuvel et al, 2013).…”

Section: Heritabilitymentioning

confidence: 99%

“…Voxel-wise analyses of network functional connectivity estimated through spatial independent component analysis (ICA) (Barber et al, 2021;Fu et al, 2015;Glahn et al, 2010;Sudre et al, 2017;Yang et al, 2016) and seed-based connectivity (Adhikari et al, 2018) have demonstrated considerable genetic influence on several resting-state networks, including sensory (Fu et al, 2015) and transmodal association networks, ranging between 0.2<h 2 <0.8 (Adhikari et al, 2018;Barber et al, 2021;Glahn et al, 2010;Sudre et al, 2017;Z. Yang et al, 2016).…”

Section: Heritabilitymentioning

confidence: 99%

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Where the genome meets the connectome: understanding how genes shape human brain connectivity

Arnatkevičiūtė¹,

Fulcher²,

Bellgrove³

et al. 2021

Preprint

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The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organisation of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

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Section: Heritabilitymentioning

confidence: 99%

Section: Heritabilitymentioning

confidence: 99%

Where the genome meets the connectome: understanding how genes shape human brain connectivity

Arnatkevičiūtė¹,

Fulcher²,

Bellgrove³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Although the recent studies have found atypical DFC in patients with ASDs, questions remain about the genetic mechanisms of higher DFC. Barber et al (2021) examined the heritability of rs-fMRI data of healthy young adults from the Human Connectome Project and found that heritability was moderate and tended to be higher for DFC than for SFC. Epidemiological studies have shown that patients with ASDs have high heritability, twin studies evaluating the heritability of ASDs showed high concordance rates (up to 90%) in monozygotic twins ( Freitag, 2007 ), and common genetic variations account for approximately 50% of the genetic risk for ASDs ( Gaugler et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Functional Connectivity Alterations and Their Associated Gene Expression Pattern in Autism Spectrum Disorders

Yuan

Wei

et al. 2022

Front. Neurosci.

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Autism spectrum disorders (ASDs) are a group of heterogeneous neurodevelopmental disorders that are highly heritable and are associated with impaired dynamic functional connectivity (DFC). However, the molecular mechanisms behind DFC alterations remain largely unknown. Eighty-eight patients with ASDs and 87 demographically matched typical controls (TCs) from the Autism Brain Imaging Data Exchange II database were included in this study. A seed-based sliding window approach was then performed to investigate the DFC changes in each of the 29 seeds in 10 classic resting-state functional networks and the whole brain. Subsequently, the relationships between DFC alterations in patients with ASDs and their symptom severity were assessed. Finally, transcription-neuroimaging association analyses were conducted to explore the molecular mechanisms of DFC disruptions in patients with ASDs. Compared with TCs, patients with ASDs showed significantly increased DFC between the right dorsolateral prefrontal cortex (DLPFC) and left fusiform/lingual gyrus, between the DLPFC and the superior temporal gyrus, between the right frontal eye field (FEF) and left middle frontal gyrus, between the FEF and the right angular gyrus, and between the left intraparietal sulcus and the right middle temporal gyrus. Moreover, significant relationships between DFC alterations and symptom severity were observed. Furthermore, the genes associated with DFC changes in ASDs were identified by performing gene-wise across-sample spatial correlation analysis between gene expression extracted from six donors’ brain of the Allen Human Brain Atlas and case-control DFC difference. In enrichment analysis, these genes were enriched for processes associated with synaptic signaling and voltage-gated ion channels and calcium pathways; also, these genes were highly expressed in autistic disorder, chronic alcoholic intoxication and several disorders related to depression. These results not only demonstrated higher DFC in patients with ASDs but also provided novel insight into the molecular mechanisms underlying these alterations.

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“…The brain is complex and variable, and more studies have proved that the changes in FC across time help reveal dynamic changes in brain function. A growing number of fMRI studies have found dynamic FC that captures the time-sensitive information about dynamic brain network (Calhoun et al, 2014 ; Pervaiz et al, 2020 ; Barber et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease

Wang

Liu

et al. 2022

Front. Comput. Neurosci.

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Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.

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Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks

Cited by 32 publications

References 86 publications

Where the genome meets the connectome: understanding how genes shape human brain connectivity

Where the genome meets the connectome: understanding how genes shape human brain connectivity

Dynamic Functional Connectivity Alterations and Their Associated Gene Expression Pattern in Autism Spectrum Disorders

Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease

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