Biology subjects

Llera Arenas, A.

Publications and source records attributed to Llera Arenas, A..

7 recordsLinked to original sources

bioRxiv2025

Integrating brain structure and function for the neurobiology and genetics of language

Brain structure and function have largely been studied separately in relation to the neurobiology and genetics of language. Here we used linked independent component analysis to integrate language network functional connectivity with brain volumetric and white matter structure in 32,677 UK Biobank participants, followed by analysis of behavioural, neurobiological and genetic correlates of the derived multimodal structure-function imaging components. Stronger functional connectivity between brain language areas was associated with increased volume of parts of the cerebellum and motor cortex, together with smaller ventricles and sensory parietal and occipital areas. The brain structure-function language components mediated an association between vocabulary level and polygenic scores for reading ability. We report 18 genomic loci associated with brain structure-function language components. Single-nucleotide polymorphism (SNP)-based heritability estimates for these components were 23-30%, and there was significant enrichment of heritability in primate-conserved genomic loci and fetal brain human-gained enhancer elements. This study revealed that structural correlates of functional language network connectivity extend well beyond previously defined language areas of the brain, and highlights the value of multimodal brain phenotyping for human neurogenetic discovery.

neuroscience↗

bioRxiv2022

Patterns of connectome variability in autism across five functional activation tasks. Fndings from the LEAP project

BackgroundAutism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioural, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterise heterogeneity in autism by identifying patterns of neural diversity as reflected in BOLD fMRI in the way individuals with autism engage with a varied array of cognitive tasks. MethodsAll analyses were based on the EU-AIMS/AIMS-2-TRIALS multisite Longitudinal European Autism Project (LEAP) with participants with autism and typically developing controls (TD) between 6 and 30 years of age. We employed a novel task-potency approach which combines the unique aspects of both resting-state fMRI and task-fMRI to quantify task-induced variations in the functional connectome. Normative modelling was used to map atypicality of features on an individual basis with respect to their distribution in neurotypical control participants. We applied robust out-of-sample canonical correlation analysis (CCA) to relate connectome data to behavioural data. ResultsDeviation from the normative ranges of global functional connectivity was greater for individuals with autism compared to TD in each fMRI task paradigm (all tasks p<0.001). The similarity across individuals of the deviation pattern was significantly increased in autistic relative to TD individuals (p<0.002). The CCA identified significant and robust brainbehavior covariation between functional connectivity atypicality and autism-related behavioral features. ConclusionsIndividuals with autism engage with tasks in a globally atypical way, but the particular spatial pattern of this atypicality is nevertheless similar across tasks. Atypicalities in the tasks originate mostly from prefrontal cortex and default mode network regions, but also speech and auditory networks. We show, moving forward, sophisticated modeling methods such as task-potency and normative modeling will prove key to unravelling complex heterogeneous conditions like autism.

neuroscience↗

bioRxiv2021

Can fMRI functional connectivity index dynamic neural communication?

In order to continuously respond to a changing environment and support self-generating cognition and behaviour, neural communication must be highly flexible and dynamic at the same time than hierarchically organized. While whole-brain fMRI measures have revealed robust yet changing patterns of statistical dependencies between regions, it is not clear whether these statistical patterns --referred to as functional connectivity-- can reflect dynamic large-scale communication in a way that is relevant to human cognition. For functional connectivity to reflect cognition, and therefore actual communication, we propose three necessary conditions: it must span sufficient temporal complexity to support the needs of cognition while still being highly organized so that the system behaves reliably; it must be able to adapt to the current behavioural context; it must exhibit fluctuations at timescales that are compatible with the timescales of cognition. To obtain reliable estimations of time-varying functional connectivity, we developed principal components of connectivity analysis (PCCA), an approach based on applying principal component analysis on multiple runs of a time-varying functional connectivity model. We use PCCA to show that functional connectivity follows low-yet multi-dimensional trajectories that can be reliably measured, and that these trajectories meet the aforementioned criteria. These analyses suggest that these trajectories might index certain aspects of communication between neural populations and support moment-to-moment cognition. Significance StatementfMRI functional connectivity is one of the most widely used metrics in neuroimaging research in both theoretical research and clinical applications. However, this work suffers from a lack of context because we still do not fully understand what fMRI functional connectivity can or cannot reflect biologically and behaviourally. In particular, can it reflect between-region neuronal communication? We develop methods to reliably quantify temporal trajectories of functional connectivity and investigate the nature of these trajectories across different experimental conditions. Using these methods, we demonstrate that functional connectivity exhibits reliable changes that are context-dependent, reflect cognitive complexity, and bear a relationship with cognitive abilities. These conditions show that fMRI functional connectivity could reflect changes in between-region communication above and beyond non-neural factors.

neuroscience↗

bioRxiv2020

Multivariate associative patterns between the gut microbiota and large-scale brain network connectivity

Research on the gut-brain axis has accelerated substantially over the course of the last years. Many reviews have outlined the important implications of understanding the relation of the gut microbiota with human brain function and behavior. One substantial drawback in integrating gut microbiome and brain data is the lack of integrative multivariate approaches that enable capturing variance in both modalities simultaneously. To address this issue, we applied a linked independent component analysis (LICA) to microbiota and brain connectivity data. We analyzed data from 58 healthy females (mean age = 21.5 years). Magnetic Resonance Imaging data were acquired using resting state functional imaging data. The assessment of gut microbial composition from feces was based on sequencing of the V4 16S rRNA gene region. We used the LICA model to simultaneously factorize the subjects large-scale brain networks and microbiome relative abundance data into 10 independent components of spatial and abundance variation. LICA decomposition resulted in four components with non-marginal contribution of the microbiota data. The default mode network featured strongly in three components, whereas the two-lateralized fronto-parietal attention networks contributed to one component. The executive-control (with the default mode) network was associated to another component. We found the abundance of Prevotella genus was associated to the strength of expression of all networks, whereas Bifidobacterium was associated with the default mode and frontoparietal-attention networks. We provide the first exploratory evidence for multivariate associative patterns between the gut microbiota and brain network connectivity in healthy humans, taking into account the complexity of both systems.

neuroscience↗

bioRxiv2019

Behavioural relevance of spontaneous, transient brain network interactions in fMRI

How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture. Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

neuroscience↗

bioRxiv2019

Spatial Patterns for Discriminative Estimation (SPADE)

Functional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP{spadesuit}DE).

neuroscience↗

bioRxiv2018

Inter-individual differences in human brain structure and morphometry link to population variation in demographics and behavior

We perform a comprehensive integrative analysis of multiple structural MR-based brain features and find strong evidence relating inter-individual structural variations to demographic and behavioral variates across a large cohort of healthy human volunteers. In particular, our findings shed some light on functional & structural integration, as we find a mode of structural variation that relates to and extends the positive-negative behavioral spectrum which was recently reported as being associated with variations in functional connectivity.\n\nSignificance statementThis work provides for the first-time strong evidence relating human brain structure variations to a wide range of demographic and behavioral measures. We show that several measures previously associated to variation in functional MRI connectivity are in fact already associated at the structural level, pointing towards structure-function integration.

neuroscience↗