The Laplacian spectrum of neural networks de Lange, Siemon C; de Reus, Marcel A; van den Heuvel, Martijn P
Frontiers in computational neuroscience,
01/2014, Letnik:
7
Journal Article
Recenzirano
Odprti dostop
The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. ...Many popular graph metrics, however, encode average properties of individual network elements. Complementing these "conventional" graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks.
Emerging adulthood is a critical neurodevelopmental stage, with alcohol use during this period consistently associated with brain abnormalities and damage in anatomical structure and white matter ...integrity. However, it is less clear how alcohol use is associated with the brain’s structural organization (i.e., white matter connections between anatomical regions). Recent connectome research has focused on rich-club regions, a collection of highly-interconnected hubs that are critical in brain communication and global network organization and disproportionately vulnerable to insults.
For the first time, we examined alcohol use associations with structural rich-club and connectome organization in emerging adults (N = 66).
Greater lifetime drinks and current monthly drinks were significantly associated with lower rich-club organization (rs =−0.38, ps < 0.003) and lower rich-club connectivity (rs <−0.34, ps < 0.007). Additionally, rich-club connectivity was significantly more negatively correlated with alcohol use than connectivity among non-rich-club regions (ps < 0.035). Examining overall structural organization, greater lifetime drinks and current monthly drinks were significantly associated with lower network density (i.e., lower network resilience; rs <−0.36, ps = 0.004). Additionally, greater lifetime drinks and current monthly drinks were significantly associated with higher network segregation (i.e., network’s tendency to divide into subnetworks; rs >0.33, ps<0.008). Alcohol use was not significantly associated with network integration (i.e., network’s efficiency in combining information across the brain; ps > 0.064).
Results provide novel evidence that alcohol use is associated with decreased rich-club connectivity and structural network disorganization. Given that both are critical in global brain communication, these results highlight the importance of examining alcohol use and brain relationships in emerging adulthood.
•Young adulthood is a critical neurodevelopmental stage with high rates of drinking.•Examined alcohol use associations with connectome organization in emerging adults.•Specifically examined rich-club connectivity and structural network organization.•Greater alcohol use was associated with decreased rich-club connectivity.•Greater alcohol use was associated with greater structural network segregation.
Human and animal nervous systems constitute complexly wired networks that form the infrastructure for neural processing and integration of information. The organization of these neural networks can ...be analyzed using the so-called Laplacian spectrum, providing a mathematical tool to produce systems-level network fingerprints. In this article, we examine a characteristic central peak in the spectrum of neural networks, including anatomical brain network maps of the mouse, cat and macaque, as well as anatomical and functional network maps of human brain connectivity. We link the occurrence of this central peak to the level of symmetry in neural networks, an intriguing aspect of network organization resulting from network elements that exhibit similar wiring patterns. Specifically, we propose a measure to capture the global level of symmetry of a network and show that, for both empirical networks and network models, the height of the main peak in the Laplacian spectrum is strongly related to node symmetry in the underlying network. Moreover, examination of spectra of duplication-based model networks shows that neural spectra are best approximated using a trade-off between duplication and diversification. Taken together, our results facilitate a better understanding of neural network spectra and the importance of symmetry in neural networks.
•Laplacian spectra provide a systems-level fingerprint of neural networks.•We examine a characteristic central peak in neural spectra.•A link is found between the central peak and patterns of wiring symmetry.
Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into ...these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.
Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased ...burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated.
A total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity.
Offspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder.
The findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.
In this article, we show that child and adolescent offspring of parents with schizophrenia had different patterns in the development of their brain’s structural connections compared with offspring of parents with bipolar disorder and offspring of parents without these conditions. The findings of this long-term study indicate that having a family history of schizophrenia is associated with changes over time during adolescence in the overall organization of the brain’s structural network.
Functional connectivity within resting-state networks (RSN-FC) is vital for cognitive functioning. RSN-FC is heritable and partially translates to the anatomic architecture of white matter, but the ...genetic component of structural connections of RSNs (RSN-SC) and their potential genetic overlap with RSN-FC remain unknown. Here, we perform genome-wide association studies (
= 24,336;
= 3412) and annotation on RSN-SC and RSN-FC. We identify genes for visual network-SC that are involved in axon guidance and synaptic functioning. Genetic variation in RSN-FC impacts biological processes relevant to brain disorders that previously were only phenotypically associated with RSN-FC alterations. Correlations of the genetic components of RSNs are mostly observed within the functional domain, whereas less overlap is observed within the structural domain and between the functional and structural domains. This study advances the understanding of the complex functional organization of the brain and its structural underpinnings from a genetics viewpoint.
Mammalian brains constitute complex organized networks of neural projections. On top of their binary topological organization, the strength (or weight) of these neural projections can be highly ...variable across connections and is thus likely of additional importance to the overall topological and functional organization of the network. Here we investigated the specific distribution pattern of connection strength in the macaque connectome. We performed weighted and binary network analysis on the cortico-cortical connectivity of the macaque provided by the unique tract-tracing dataset of Markov and colleagues (2014) and observed in both analyses a small-world, modular and rich club organization. Moreover, connectivity strength showed a distribution augmenting the architecture identified in the binary network version by enhancing both local network clustering and the central infrastructure for global topological communication and integration. Functional consequences of this topological distribution were further examined using the Kuramoto model for simulating interactions between brain regions and showed that the connectivity strength distribution across connections enhances synchronization within modules and between rich club hubs. Together, our results suggest that neural pathway strength promotes topological properties in the macaque connectome for local processing and global network integration.
High-resolution tract-tracing methods allow for the measurement of cortico-cortical anatomical pathways and quantification of their connectivity strength. The variable strength of neural pathways holds additional information on the complex organization of brain networks. We studied connectivity strength in the macaque by using the open-access connectome dataset of Markov and colleagues (2014), which contains high-resolution information on the number of axonal projections interconnecting brain regions. Our results show that connectivity strength adopts a nonrandom distribution and enhances key network features already present in the binary version of the network. Using the Kuramoto model to simulate functional dynamics, we further demonstrated that connectivity strength enhances local processing and global network integration. This study aids in the understanding of the macaque connectome by clarifying the role of connectivity strength in the network topology and functioning.
Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be ...associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if they do, how these effects could contribute to the disease course of MDD.
This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic patients with MDD and 539 healthy control subjects.
Polygenic risk score for T2D across MDD patients and healthy control subjects was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this fractional anisotropy variation may mediate the association between polygenic risk score and cognitive performance.
Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in patients with MDD and healthy control subjects, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments.
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