Biology subjects

Messaritaki, E.

Publications and source records attributed to Messaritaki, E..

9 recordsLinked to original sources

bioRxiv2026

Myelin-weighted Core Network Connectivity Supports Trait-Level Future Thinking

Why are some individuals able to recollect past events and imagine future events more richly than others? Here we examine how structural connectivity within a 'core network' supporting mnemonic cognition, substantially overlapping with the canonical default network, underpins inter-individual variation in autobiographical thought. We analysed state-of-the-art magnetic resonance imaging data from 109 adults collected on a high-gradient 3T Connectom scanner, in relation to their Survey of Autobiographical Memory (SAM) scores - a psychometric measure of self-reported episodic autobiographical, semantic, spatial, and future thinking abilities. We anatomically defined a core network, employed streamline tractography to derive participant-specific structural brain networks, and weighted their connections with the myelin water fraction derived from quantitative multicomponent relaxometry. We investigated associations between graph theoretical metrics of the core network and self-reported mnemonic abilities. We found a negative association between future thinking abilities and characteristic path length, and a positive association between future thinking abilities and mean clustering coefficient. Further analysis showed that the node-specific clustering coefficient of most core network nodes was positively correlated with future thinking scores, with frontotemporal nodes showing the strongest associations. Our findings indicate that both efficient integration and local segregation of the core network are important for trait-level future thinking.

neuroscience↗

bioRxiv2026

From Brain Microstructure to Dynamics: Linking Grey and White Matter Architecture to Propagation Delays

Understanding the relationship between neural dynamics and underlying brain structure, and in particular the impact of the latter on conduction delays, remains a core question in neuroscience. In humans, we primarily access these phenomena at the macroscale through neurophysiological measures of propagation. At this scale, signal propagation is reflected in measurable delays arising from both white matter (WM) axonal conduction and grey matter (GM) synaptic and integrative processes. While specific WM features have been explicitly linked to single-axon conduction delays, no clear models link GM microstructural properties to large-scale propagation. To this aim, we combined advanced MRI microstructural modelling with resting-state MEG to link brain structure to signal propagation in a sample of 94 healthy controls. WM metrics included axonal diameter, myelination (g-ratio, myelin water fraction), and tract length, while GM was characterised using the SANDI model and cortical thickness. Propagation delays were quantified using the neuronal avalanches framework. Our findings indicated significant associations between all of WM and GM metrics and the propagation delays. When attempting to use our W/GM metrics we were able to predict up to ~26% of the observed delay using linear regression modelling. We also found associations between frequency-specific propagation dynamics and tract length. Finally, using a canonical correlation analysis we demonstrated multivariate coupling between our W/GM metrics and the MEG frequency-specific propagation delays. Our findings provide evidence for the link between tissue structure and large-scale neural dynamics, supporting the development of biologically grounded models of signal propagation.

neuroscience↗

bioRxiv2024

Deep-learning and analytical models give distinct results for the brain structure-function relationship in health and in psychosis

Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a comprehensive framework for mapping structural connectivity measured via diffusion-MRI to resting-state functional connectivity measured via magnetoencephalography, utilizing a deep-learning model based on a Graph Multi-Head Attention AutoEncoder. We compare the results to those from an analytical model that utilizes shortest-path-length and search-information communication mechanisms. The deep-learning model outperformed the analytical model in predicting functional connectivity in healthy participants at the individual level, achieving mean correlation coefficients higher than 0.8 in the alpha and beta frequency bands. Our results imply that human brain structural connectivity and electrophysiological functional connectivity are tightly coupled. The two models suggested distinct structure-function coupling in people with psychosis compared to healthy participants (p < 2 x 10-4 for the deep-learning model, p < 3 x 10-3 in the delta band for the analytical model). Importantly, the alterations in the structure-function relationship were much more pronounced than any structure-specific or function-specific alterations observed in the psychosis participants. The findings demonstrate that analytical algorithms effectively model communication between brain areas in psychosis patients within the delta and theta bands, whereas more sophisticated models are necessary to capture the dynamics in the alpha and beta band.

neuroscience↗

bioRxiv2023

Graph Laplacian Spectrum of Structural Brain Networks is Subject-Specific, Repeatable but Highly Dependent on Graph Construction Scheme

It has been proposed that the estimation of the normalized graph Laplacian over a brain networks spectral decomposition can reveal the connectome harmonics (eigenvectors) corresponding to certain frequencies (eigenvalues). Here, I used test-retest dMRI data from the Human Connectome Project to explore the repeatability, and the influence of graph construction schemes on a) graph Laplacian spectrum, b) topological properties, c) high-order interactions (3,4-motifs,odd-cycles), and d) their associations on structural brain networks (SBN). Additionally, I investigated the performance of subjects identification accuracy (brain fingerprinting) of the graph Laplacian spectrum, the topological properties, and the high-order interactions. Normalized Laplacian eigenvalues were found to be subject-specific and repeatable across the five graph construction schemes. The repeatability of connectome harmonics is lower than that of the Laplacian eigenvalues and shows a heavy dependency on the graph construction scheme. A repeatable relationship between specific topological properties of the SBN with the Laplacian spectrum was also revealed. The identification accuracy of normalized Laplacian eigenvalues was absolute (100%) across the graph construction schemes, while a similar performance was observed for a combination of topological properties of SBN (communities,3,4-motifs, odd-cycles) only for the 9m-OMST. Collectively, Laplacian spectrum, topological properties, and high-order interactions characterized uniquely SBN.

neuroscience↗

bioRxiv2022

Increased sensorimotor structural connectivity in high schizotypy

The link between brain structural connectivity and schizotypy was explored in two healthy-participant cohorts, collected at two different neuroimaging centres, comprising 140 and 115 participants respectively. The participants completed the Schizotypal Personality Questionnaire (SPQ), through which their schizotypy scores were calculated. Diffusion-MRI data were used to perform tractography and to generate the structural brain networks of the participants. The edges of the networks were weighted with the inverse radial diffusivity. Graph theoretical metrics of the default-mode, sensorimotor, visual and auditory subnetworks were derived and their correlation coefficients with the schizotypy scores were calculated. To the best of our knowledge, this is the first time that graph theoretical measures of structural brain networks are investigated in relation to schizotypy. A positive correlation was found between the schizotypy score and the mean node degree and mean clustering coefficient of the sensorimotor and the default-mode subnetworks. The nodes driving these correlations were the right postcentral gyrus, the left paracentral lobule, the right superior frontal gyrus, the left parahippocampal gyrus and the bilateral precuneus, i.e., nodes that exhibit compromised functional connectivity in schizophrenia. Implications for schizophrenia and schizotypy are discussed.

neuroscience↗

bioRxiv2021

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

We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimers disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Diffusion MRI data were used to perform whole-brain tractography and to generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participants genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS 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 was negatively correlated with the genomewide PRS (r=-0.19, p=1.3x10-5), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r=0.16, p=9.2x10-4), and the mean clustering coefficient was negatively correlated with the tau protein binding PRS (r=-0.16, p=9.2x10-4). In the default mode network, the mean nodal strength was negatively correlated with the genomewide PRS (r=-0.14, p=1.5x10-3). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r=-0.16, p=3.7x10-4; r=-0.15, p=8.8x10-4). Our results indicate small reductions in brain connectivity in young adults at risk of developing Alzheimers disease in later life.

neuroscience↗

bioRxiv2020

Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis.

BackgroundAlzheimers Disease (AD) is the most common form of dementia with genetic and environmental risk contributing to its development. Graph theoretical analyses of brain networks constructed from structural and functional MRI measurements have identified connectivity changes in AD and individuals with mild cognitive impairment (MCI). However, brain connectivity in asymptomatic individuals at risk of AD remains poorly understood. MethodsWe analysed diffusion-weighted magnetic resonance imaging (dMRI) data from 160 asymptomatic individuals (38-71 years) from the Cardiff Ageing and Risk of Dementia Study (CARDS). We calculated white matter tracts and constructed whole-brain, default-mode-network and visual structural brain networks that incorporate multiple structural metrics as edge weights. We then calculated the relationship of three AD risk factors, namely Apolipoprotein-E {varepsilon}4 genotype (APOE4), family history (FH) of dementia, and central obesity, on graph theoretical measures and hubs. ResultsWe observed no risk-related differences in clustering coefficients, characteristic path lengths, eccentricity, diameter and radius across the whole-brain, default-mode-network or visual system. However, a hub in the right paracentral lobule was present in all high-risk groups (FH, APOE4, obese) but absent in low-risk groups (no FH, APOE4-ve, healthy weight). DiscussionWe identified no risk-related effects on graph theoretical metrics in the structural brain networks of cognitively healthy individuals. However, high-risk was associated with a hub in the right paracentral lobule, an area with motor and sensory functions related to the lower limb. If this phenotype is shown to predict symptom development in longitudinal studies, it could be used as an early biomarker of AD. Impact StatementAlzheimers Disease is a common form of dementia which to date has no cure. Identifying early biomarkers will aid the discovery and development of treatments that may slow AD progression in the future. In this paper we report that asymptomatic individuals at heightened risk of dementia due to their family history, Apolipoprotein-E {varepsilon}4 genotype and body adiposity have a hub in the right paracentral lobule which is absent in low-risk groups. If this phenotype were to predict the development of symptoms in a longitudinal study of the same cohort, it could provide an early biomarker of disease progression.

neuroscience↗

bioRxiv2020

Predicting MEG resting-state functional connectivity using microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries at the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology for better understanding of functional mechanisms. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

neuroscience↗

bioRxiv2019

Optimization of graph construction can significantly increase the power of structural brain network studies

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility. Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics, while data-driven schemes performed better for global metrics. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a large factor depending on the scheme used, affecting the power of studies to reveal the effects of interest.

neuroscience↗