FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease.

Measuring transient functional connectivity is an important challenge in electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high-temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a methodology to overcome these problems called filter average short-term (FAST) functional connectivity.

A statistical mechanics investigation of unfolded protein response across organisms.

Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases controls in two large population samples.

Background: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes.

FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease.

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity.

Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding.

Alzheimer's Disease (AD) shows both complex alterations of functional dependencies between brain regions and a decreased ability to perform Visual Short-Term Memory Binding (VSTMB) tasks. Recent advances in network neuroscience toward understanding the complexity of hierarchical brain function here enables us to establish a link between these two phenomena. Here, we study data on two types of dementia at Mild Cognitive Impairment (MCI) stage-familial AD patients (E280A mutation of the presenilin-1 gene) and elderly MCI patients at high risk of sporadic AD, both with age-matched controls.

A computational exploration of resilience and evolvability of protein-protein interaction networks.

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience.

Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability.

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach.

Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space.

Networks of disparate phenomena-be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions-exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Building on recurring trends in the literature, the theory asserts that links arise due to individualistic as well as dyadic information and that important dyadic information making up the so-called depth factor is obscured by this essentially non-dyadic information making up the surface factor.

Hierarchical Complexity of the Macro-Scale Neonatal Brain.

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity.

Pipeline comparisons of convolutional neural networks for structural connectomes: predicting sex across 3,152 participants.

With several initiatives well underway towards amassing large and high-quality population-based neuroimaging datasets, deep learning is set to push the boundaries of what is possible in classification and prediction in neuroimaging studies. This includes those that derive increasingly popular structural connectomes, which map out the connections (and their relative strengths) between brain regions. Here, we test different Convolutional Neural Network (CNN) models in a benchmark sex prediction task in a large sample of N=3,152 structural connectomes acquired from the UK Biobank, and compare results across different connectome processing choices.

Normalised degree variance.

Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network science studies. The degree variance is an important metric for characterising network degree heterogeneity. Here, we provide an analytically valid normalisation of degree variance to replace previous normalisations which are either invalid or not applicable to all networks.

On neighbourhood degree sequences of complex networks.

Network topology is a fundamental aspect of network science that allows us to gather insights into the complicated relational architectures of the world we inhabit. We provide a first specific study of neighbourhood degree sequences in complex networks. We consider how to explicitly characterise important physical concepts such as similarity, heterogeneity and organization in these sequences, as well as updating the notion of hierarchical complexity to reflect previously unnoticed organizational principles.