The impact of genetic risk for Alzheimers disease on the structural brain networks of young adults

To facilitate pre-symptomatic diagnosis of late-onset Alzheimers disease, non-invasive imaging biomarkers could be combined with genetic risk information. In this work, we investigated the structural brain networks of young adults in relation to polygenic risk for Alzheimers disease, using magnetic resonance imaging (MRI) and genotype data for 564 19-year-old participants from the Avon Longitudinal Study of Parents and Children. Diffusion MRI was acquired on a 3T scanner, and the data were used to perform whole-brain tractography. The resulting tractograms were used to generate structural brain networks, using the number of streamlines and the diffusion tensor fractional anisotropy as edge weights. This was done for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. Graph theoretical metrics were calculated for these networks, for each participant. The hubs of the networks were also identified, and the connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating the burden of genetic risk carried by an individual, were calculated both at genome-wide level and for nine specific disease pathways. The correlation coefficients were calculated between the PRSs and a) the graph theoretical metrics of the structural networks and b) the rich-club, feeder and local connectivity of the whole brain networks. In the visual subnetwork, the mean nodal strength exhibited a negative correlation with the genome-wide PRS including the APOE locus, while the mean betweenness centrality showed a positive correlation with the pathway-specific PRS for plasma lipoprotein particle assembly including the APOE locus. The rich-club connectivity was reduced in participants with higher genome-wide PRS including the APOE locus. Our results indicate small changes in the brain connectome of young adults at risk of developing Alzheimers disease in later life

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Heritability of hierarchical structural brain network

10.1101/209635 ◽

2017 ◽

Author(s):

Moo K. Chung ◽

Zhan Luo ◽

Nagesh Adluru ◽

Andrew L. Alexander ◽

Davidson J. Richard ◽

...

Keyword(s):

Diffusion Weighted Imaging ◽

Brain Networks ◽

Structural Connectivity ◽

Imaging Study ◽

Brain Network ◽

Genetic Contribution ◽

Whole Brain ◽

Diffusion Weighted ◽

Structural Brain Network ◽

Structural Brain

ABSTRACTWe present a new structural brain network parcellation scheme that can subdivide existing parcellations into smaller subregions in a hierarchically nested fashion. The hierarchical parcellation was used to build multilayer convolutional structural brain networks that preserve topology across different network scales. As an application, we applied the method to diffusion weighted imaging study of 111 twin pairs. The genetic contribution of the whole brain structural connectivity was determined. We showed that the overall heritability is consistent across different network scales.

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Functional Brain Networks and Alcohol Consumption: From the Naïve State to Chronic Heavy Drinking

10.1101/2020.09.14.296871 ◽

2020 ◽

Author(s):

Jared A. Rowland ◽

Jennifer R. Stapleton-Kotloski ◽

Greg E. Alberto ◽

April T. Davenport ◽

Phillip M. Epperly ◽

...

Keyword(s):

Alcohol Consumption ◽

Alcohol Use ◽

Brain Networks ◽

Alcohol Exposure ◽

Brain Network ◽

Free Access ◽

Functional Brain ◽

Rich Club ◽

Functional Brain Networks ◽

The Rich

AbstractA fundamental question for alcohol use disorder is how naïve brain networks are reorganized in response to the consumption of alcohol. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during the transition from naïve, to early, to chronic consumption. Resting-state brain networks of six female monkeys were acquired using magnetoencephalography prior to alcohol exposure, after early exposure, and after free-access to alcohol using a well-established model of chronic heavy alcohol use. Functional brain network metrics were derived at each time point. Assortativity, average connection frequency, and number of gamma connections changed significantly over time. All metrics remained relatively stable from naïve to early drinking, and displayed significant changes following increased quantity of alcohol consumption. The assortativity coefficient was significantly less negative (p=.043), connection frequency increased (p=.03), and gamma connections increased (p=.034). Further, brain regions considered hubs (p=.037) and members of the Rich Club (p=.012) became less common across animals following the introduction of alcohol. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r=-.88, p<.001). Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters the topology of functional brain networks, shifting hubs and Rich Club membership away from previous regions in a non-systematic manner. Further work to refine these relationships may lead to the identification of a high-risk AUD phenotype.

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Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder

Psychological Medicine ◽

10.1017/s0033291718001150 ◽

2018 ◽

Vol 49 (3) ◽

pp. 510-518 ◽

Cited By ~ 21

Author(s):

Ying Wang ◽

Feng Deng ◽

Yanbin Jia ◽

Junjing Wang ◽

Shuming Zhong ◽

...

Keyword(s):

Bipolar Disorder ◽

Graph Theory ◽

White Matter ◽

Diffusion Tensor ◽

Whole Brain ◽

Rich Club ◽

Brain Connectome ◽

Structural Connectome ◽

Healthy Control ◽

Structural Brain

AbstractBackgroundBipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD.MethodsForty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters.ResultsCompared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density.ConclusionsOur results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.

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P.384 Alzheimer's polygenic risk score and structural brain networks in young adults

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2020.09.287 ◽

2020 ◽

Vol 40 ◽

pp. S220-S221

Author(s):

A. Mirza-Davies ◽

S. Foley ◽

X. Caseras ◽

D. Jones ◽

J. Harrison ◽

...

Keyword(s):

Young Adults ◽

Risk Score ◽

Brain Networks ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Structural Brain

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Breakdown of Whole-brain Dynamics in Preterm-born Children

Cerebral Cortex ◽

10.1093/cercor/bhz156 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1159-1170 ◽

Cited By ~ 3

Author(s):

Nelly Padilla ◽

Victor M Saenger ◽

Tim J van Hartevelt ◽

Henrique M Fernandes ◽

Finn Lennartsson ◽

...

Keyword(s):

Activity Patterns ◽

Brain Magnetic Resonance Imaging ◽

Imaging Data ◽

Whole Brain ◽

Rich Club ◽

Extremely Preterm ◽

The Rich ◽

Preterm Born ◽

Brain Reorganization ◽

The Brain

Abstract The brain operates at a critical point that is balanced between order and disorder. Even during rest, unstable periods of random behavior are interspersed with stable periods of balanced activity patterns that support optimal information processing. Being born preterm may cause deviations from this normal pattern of development. We compared 33 extremely preterm (EPT) children born at < 27 weeks of gestation and 28 full-term controls. Two approaches were adopted in both groups, when they were 10 years of age, using structural and functional brain magnetic resonance imaging data. The first was using a novel intrinsic ignition analysis to study the ability of the areas of the brain to propagate neural activity. The second was a whole-brain Hopf model, to define the level of stability, desynchronization, or criticality of the brain. EPT-born children exhibited fewer intrinsic ignition events than controls; nodes were related to less sophisticated aspects of cognitive control, and there was a different hierarchy pattern in the propagation of information and suboptimal synchronicity and criticality. The largest differences were found in brain nodes belonging to the rich-club architecture. These results provide important insights into the neural substrates underlying brain reorganization and neurodevelopmental impairments related to prematurity.

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Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke

Stroke ◽

10.1161/strokeaha.120.031541 ◽

2021 ◽

Vol 52 (6) ◽

pp. 2115-2124

Author(s):

Philip Egger ◽

Giorgia G. Evangelista ◽

Philipp J. Koch ◽

Chang-Hyun Park ◽

Laura Levin-Gleba ◽

...

Keyword(s):

Brain Networks ◽

Small Lesion ◽

Lesion Volume ◽

Good Recovery ◽

Imaging Data ◽

Brain Organization ◽

Rich Club ◽

Network Analyses ◽

Poor Recovery ◽

The Rich

Background and Purpose: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. Methods: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. Results: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the metric to be the sum of the entries in the disconnectome and, more importantly, the to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPR<LLPR), for , this relationship was reversed (MLPR>LLPR) or showed no difference for . Conclusions: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

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Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study

Brain Sciences ◽

10.3390/brainsci11070938 ◽

2021 ◽

Vol 11 (7) ◽

pp. 938

Author(s):

Maliheh Ahmadi ◽

Kamran Kazemi ◽

Katarzyna Kuc ◽

Anita Cybulska-Klosowicz ◽

Mohammad Sadegh Helfroush ◽

...

Keyword(s):

Resting State ◽

Phase Synchronization ◽

Brain Networks ◽

Source Analysis ◽

Group Differences ◽

Rich Club ◽

Eyes Closed ◽

Eeg Source Analysis ◽

Functional Brain Networks ◽

The Rich

Growing evidence indicates that disruptions in the brain’s functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

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Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics

Journal of the International Neuropsychological Society ◽

10.1017/s1355617715000867 ◽

2016 ◽

Vol 22 (2) ◽

pp. 240-249 ◽

Cited By ~ 20

Author(s):

Ronald A. Yeo ◽

Sephira G. Ryman ◽

Martijn P. van den Heuvel ◽

Marcel A. de Reus ◽

Rex E. Jung ◽

...

Keyword(s):

Path Length ◽

Brain Networks ◽

Principal Component ◽

Group Differences ◽

Healthy Controls ◽

Characteristic Path Length ◽

Rich Club ◽

Graph Metrics ◽

The Rich ◽

Multi Center Study

AbstractObjectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA. (JINS, 2016, 22, 240–249)

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Rich Club Characteristics of Alcohol-Naïve Functional Brain Networks Predict Future Drinking Phenotypes in Rhesus Macaques

Frontiers in Behavioral Neuroscience ◽

10.3389/fnbeh.2021.673151 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jared A. Rowland ◽

Jennifer R. Stapleton-Kotloski ◽

Greg E. Alberto ◽

April T. Davenport ◽

Phillip M. Epperly ◽

...

Keyword(s):

Alcohol Consumption ◽

Brain Networks ◽

Alcohol Exposure ◽

Brain Network ◽

Free Access ◽

Functional Brain ◽

Rich Club ◽

Functional Brain Network ◽

Functional Brain Networks ◽

The Rich

Purpose: A fundamental question for Alcohol use disorder (AUD) is how and when naïve brain networks are reorganized in response to alcohol consumption. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during transition from the naïve state to chronic consumption.Procedures: Resting-state brain networks of six female rhesus macaque (Macaca mulatta) monkeys were acquired using magnetoencephalography (MEG) prior to alcohol exposure and after free-access to alcohol using a well-established model of chronic heavy alcohol consumption. Functional brain network metrics were derived at each time point.Results: The average connection frequency (p < 0.024) and membership of the Rich Club (p < 0.022) changed significantly over time. Metrics describing network topology remained relatively stable from baseline to free-access drinking. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r = −0.88, p < 0.001).Conclusions: Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters functional brain networks, shifting hubs and Rich Club membership away from previous regions in a non-systematic manner. Further work to refine these relationships may lead to the identification of a high-risk drinking phenotype.

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Trajectory of rich club properties in structural brain networks

10.1101/2021.11.16.468806 ◽

2021 ◽

Author(s):

Levin Riedel ◽

Martijn P van den Heuvel ◽

Sebastian Markett

Keyword(s):

Brain Networks ◽

Structural Connectivity ◽

Subsequent Development ◽

Brain Network ◽

Aging Brain ◽

Childhood And Adolescence ◽

Rich Club ◽

Age Related ◽

Connectivity Strength ◽

Structural Brain

Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8,066 participants aged 5 to 80 years. Across all brain regions, structural connectivity strength followed an ′inverted-U′-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a ′first come, last served′ model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.

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