Biology subjects

Erramuzpe, A.

Publications and source records attributed to Erramuzpe, A..

5 recordsLinked to original sources

bioRxiv2025

Brain structural modules associated to functional high-order interactions in the human brain,

The brains modular organization, ranging from microcircuits to large-scale networks, has been extensively studied in terms of its structural and functional properties. Particularly insightful has been the investigation of the coupling between structural connectivity (SC) and functional connectivity (FC), whose analysis has revealed important insights into the brains efficiency and adaptability related to various cognitive functions. Interestingly, links in SC are intrinsically pairwise but this is not the case for FC; and while recent work demonstrates the relevance of the brains high-order interactions (HOI), the coupling of between SC and functional HOI remains unexplored. To address this gap, this study leverages functional MRI and diffusion weighted imaging to delineate the brains modular structure by investigating the coupling between SC and functional HOI. Our results demonstrates that structural networks can be associated with both redundant and synergistic functional interactions. In particular, SC exhibits both positive and negative correlations with redundancy, it shows consistent positive correlations with synergy, indicating that a higher density of structural connections is linked to increased synergistic interactions. These findings advance our understanding of the complex relationship between structural and high-order functional properties, shedding light on the brains architecture underlying its modular organization. Significance StatementUnderstanding the intricate relationship between the brain structural and functional connectivity is instrumental for enabling a richer characterization of cognitive functions. This challenge is complex, since it involves comparing pairwise links representing white-matter fibers with higher-order functional interactions that can involve multiple brain regions sharing information -- indeed, these interactions can be redundant in presence of shared information, or synergistic when novel information arises in the whole subsystem. Here we advance the investigation of the coupling between brain structure and high-order interactions, offering new insights into how structural properties are associated with both redundant and synergistic functional modules. Furthermore, the approach presented here opens new doors to investigate how this coupling between structural connectivity and functional HOI is modulated during development or under diverse brain pathologies.

neuroscience↗

bioRxiv2024

AgeML: Age modelling with Machine Learning

An approach to age modeling involves the supervised prediction of age using machine learning from subject features. The derived age metrics are used to study the relationship between healthy and pathological aging in multiple body systems, as well as the interactions between them. We lack a standard for this type of age modeling. In this work we developed AgeML, an OpenSource software for age-prediction from any type of tabular clinical data following well-established and tested methodologies. The objective is to set standards for reproducibility and standardization of reporting in supervised age modeling tasks. AgeML does age modeling, calculates age deltas, the difference between predicted and chronological age, measures correlations between age deltas and factors, visualizes differences in age deltas of different clinical populations and classifies clinical populations based on age deltas. With this software we are able to reproduce published work and unveil novel relationships between body organs and polygenetic risk scores. AgeML is age modeling made easy for standardization and reproducibility.

bioinformatics↗

bioRxiv2023

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions. Addressing this issue, we provide open datasets and code enabling the examination of the correspondence between structural and functional connectivities at different scales. We present a module-level strategy that overcomes region-level approaches in understanding the structure-function correspondence. Moreover, we also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

neuroscience↗

bioRxiv2020

A controlled thermoalgesic stimulation device to identify novel pain perception biomarkers

Objective To develop a new device that will help identify physiological markers of pain perception by reading the brain’s electrical activity and the bodies hemodynamic interactions while applying thermoalgesic stimulation. Methods: We designed a compact prototype that generates well-controlled thermal stimuli using a computer driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals as the stimuli are varied. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females) and to account for the inter-subject variability in the tolerance to thermal pain, we first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increases the temperature. Subsequently, we defined the pain parameters associated with a stimulation temperature equivalent to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. Results: Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals could differentiate the condition of pain. In particular, the PPG SE was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. Conclusion: Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. Significance: The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2017

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

Physiological ageing affects brain structure and function impacting its morphology, connectivity and performance. However, at which extent brain-connectivity metrics reflect the age of an individual and whether treatments or lifestyle factors such as physical activity influence the age-connectivity match is still unclear. Here, we assessed the level of physical activity and collected brain images from healthy participants (N=155) ranging from 10 to 80 years to build functional (resting-state) and structural (tractography) connectivity matrices that were combined as connectivity descriptors. Connectivity descriptors were used to compute a maximum likelihood age estimator that was optimized by minimizing the mean absolute error. The connectivity-based estimated age, i.e. the brain-connectome age (BCA), was compared to the chronological age (ChA). Our results were threefold. First, we showed that ageing widely affects the structural-functional connectivity of multiple structures, such as the anterior part of the default mode network, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus and temporal pole. Second, our analysis showed that the structure-function connectivity between basal ganglia and thalamus to orbitofrontal and frontal areas make a major contribution to age estimation. Third, we found that high levels of physical activity reduce BCA as compared to ChA, and vice versa, low levels increment it. In conclusion, the BCA model results highlight the impact of physical activity and the key role played by the connectivity between basal ganglia and thalamus to frontal areas on the process of healthy aging. Notably, the same methodology can be generally applied both to evaluate the impact of other factors and therapies on brain ageing, and to identify the structural-functional brain connectivity correlate of other biomarkers than ChA.

neuroscience↗