scholarly journals How structural brain network topologies associate with cognitive abilities in a value-based decision-making task

2019 ◽  
Author(s):  
Cristina Bañuelos ◽  
Timothy Verstynen
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Principal Components ◽  
Iowa Gambling Task ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Brain Network ◽  
Gambling Task ◽  
Structural Brain Network ◽  
Structural Brain

Value-based decision-making relies on effective communication across disparate brain networks. Given the scale of the networks involved in adaptive decision-making, variability in how they communicate should impact behavior; however, precisely how the topological pattern of structural connectivity of individual brain networks influences individual differences in value-based decision-making remains unclear. Using diffusion MRI, we measured structural connectivity networks in a sample of community dwelling adults (N=124). We used standard graph theoretic measures to characterize the topology of the networks in each individual and correlated individual differences in these topology measures with differences in the Iowa Gambling Task. A principal components regression approach revealed that individual differences in brain network topology associate with differences in optimal decision-making, as well as associate with differences in each participant’s sensitivity to high frequency rewards. These findings show that aspects of structural brain network organization can constrain how information is used in value-based decision-making.AbbreviationsMRI - Magnetic Resonance Imaging; IGT – Iowa Gambling Task; DWI – Diffusion Weighted Imaging; QSDR – Q-Space Diffeomorphic Reconstruction; PCA – Principal Components Analysis; GLM – Generalized Linear Models

scholarly journals Heritability of hierarchical structural brain network

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.

scholarly journals Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

2020 ◽  
Author(s):  
Lu Wang ◽  
Feng Vankee Lin ◽  
Martin Cole ◽  
Zhengwu Zhang
Keyword(s):  
Oral Reading ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Learning Differences ◽  
Structural Brain Network ◽  
Levels Of Functioning ◽  
The Brain ◽  
Variable Selection Problem ◽  
Structural Brain

AbstractStructural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

scholarly journals Trajectory of rich club properties in structural brain networks

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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