Biology subjects

Llera, A.

Publications and source records attributed to Llera, A..

10 recordsLinked to original sources

bioRxiv2025

Independent Filter Analysis for Group Discrimination in fMRI

Traditional group-level fMRI analysis approaches, such as Independent Component Analysis (ICA), often rely on unsupervised dimensionality reduction to map subjects into a common feature space. While effective for capturing common variance across all subjects, the preservation of discriminative features between groups of participants is not guaranteed. To address this limitation, we introduce Independent Filter Analysis (IFA), a supervised extension of group ICA that explicitly models group-discriminative information as part of the dimensionality reduction steps. Prior to unmixing, IFA constructs a subspace that simultaneously retains both shared and group-specific information, enhancing sensitivity to group effects while preserving biological interpretability. We validated IFA using simulated data and paired condition comparisons from three Human Connectome Project (HCP) tasks. In the simulation, IFA achieved 95% classification accuracy, outperforming group ICA, which failed to detect subtle group differences. On the HCP data, IFA increased network matrix classification accuracy by up to 15% and produced spatial maps that more precisely reflected task-relevant differences.

neuroscience↗

bioRxiv2025

The Neural Architecture of Dream Recall Frequency: Insights from Interindividual Variations in Brain Structure and Function

Dreaming represents a complex and universal aspect of human sleep, yet it remains an intriguing phenomenon, with the neural mechanisms underlying dream experiences and their frequency not fully understood. This study employs a multimodal neuroimaging approach, integrating quantitative multi-parameter mapping, diffusion tensor imaging, and resting-state functional MRI, to investigate the neural correlates of dream recall frequency (DRF) in a large cohort of 258 healthy individuals. By employing Linked Independent Component Analysis (LICA), we were able to discern distinctive patterns of brain structure and function that correlated with variations in DRF. Our findings elucidate a complex relationship between dream recall and brain microstructure integrity, particularly in white matter regions of the orbitofrontal cortex, parahippocampal gyrus, superior parietal lobule, and occipital cortex. Higher DRF was related to increased white matter microstructure integrity in these regions and decreased gray matter volume in occipital and temporal areas. In terms of functional measures, higher DRF was associated with reduced connectivity across a range of resting-state networks, including the default mode, visual, and dorsal attention networks. This was particularly evident in the right precuneus and posterior cingulate cortex. These results suggest that enhanced dream recall may be related to the organization of higher-order visual and cognitive processing areas, supporting a top-down model of dreaming. This study contributes to a more comprehensive understanding of the neural substrates underlying individual differences in dream recall, offering a foundation for future investigations into the neurobiology and causal relationships of dreaming.

neuroscience↗

bioRxiv2024

Linking functional and structural brain organisation with behaviour in healthy adults

Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.

neuroscience↗

bioRxiv2022

Fine-grained topographic organization within somatosensory cortex during resting-state and emotional face-matching task and its association with ASD traits

BACKGROUNDSensory atypicalities are particularly common in autism spectrum disorders (ASD). Nevertheless, our knowledge about the divergence of the underlying somatosensory region and its association with ASD phenotype features is limited. METHODSWe applied a data-driven approach to map the fine-grained variations in functional connectivity of the primary somatosensory cortex (S1) to the rest of the brain in 240 autistic and 164 neurotypical individuals from the EU-AIMS LEAP dataset, aged between 7 and 30. We estimated the S1 connection topography ( connectopy) during rest and during the emotional face-matching (Hariri) task, an established measure of emotion reactivity, and accessed its association with a set of clinical and behavioral variables. RESULTSWe demonstrated that the S1 connectopy is organized along a dorsoventral axis, mapping onto the somatotopic organization of S1. We found that its spatial characteristics were linked to the individuals adaptive functioning skills, as measured by the Vineland Adaptive Behavior Scales, across the whole sample. Higher functional differentiation characterized the S1 connectopies of individuals with higher daily life adaptive skills. Notably, we detected significant differences between rest and the Hariri task in the S1 connectopies, as well as their projection maps onto the rest of the brain suggesting a task-modulating effect on S1 due to emotion processing. CONCLUSIONSVariation of daily life adaptive skills appears to be reflected in the brains mesoscale neural circuitry, as shown by the S1 connectivity profile, which is also differentially modulated during rest and emotional processing.

neuroscience↗

bioRxiv2022

Autism is associated with inter-individual variations of gray and white matter morphology

BackgroundAlthough many studies have explored atypicalities in gray and white matter (GM, WM) morphology of autism, most of them rely on unimodal analyses that do not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish multimodal brain patterns that differentiate between autism and typically developing (TD) controls and explore associations between these brain patterns and clinical measures. MethodsWe studied 183 individuals with autism and 157 TD individuals (6-30 years) in a large deeply phenotyped autism dataset (EU-AIMS LEAP). Linked Independent Component Analysis was utilized to link all participants GM and WM images, and group comparisons of modality shared variances were examined. Subsequently, we performed a canonical correlation analysis to explore the aggregated effects between all multimodal GM-WM covariations and clinical profiles. ResultsOne multimodal pattern was significantly related to autism. This pattern was primarily associated with GM in bilateral insula, frontal, pre- and post-central, cingulate, and caudate areas, and co-occurred with altered WM features in the superior longitudinal fasciculus. The canonical analysis showed a significant multivariate correlation primarily between multimodal brain patterns that involved variation of corpus callosum, and symptoms of social affect in the autism group. ConclusionsOur findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism, and show that the complex clinical autism phenotype can be interpreted by multimodal brain patterns that are spread across the brain involving both cortical and subcortical areas.

neuroscience↗

bioRxiv2021

Multimodal lesion network mapping to predict sensorimotor behavior in stroke patients

Lesion network mapping (LNM) has proved to be a successful technique to map symptoms to brain networks after acquired brain injury. Beyond the characteristics of a lesion, such as its etiology, size or location, LNM has shown that common symptoms in patients after injury may reflect the effects of their lesions on the same circuits, thereby linking symptoms to specific brain networks. Here, we extend LNM to its multimodal form, using a combination of functional and structural connectivity maps drawn from data from 1000 healthy participants in the Human Connectome Project. We applied the multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Test scores were predicted using a Canonical Correlation Analysis with multimodal brain maps as independent variables, and cross-validation strategies were employed to overcome overfitting. The results obtained led us to draw three conclusions. First, the multimodal analysis reveals how functional connectivity maps contribute more than structural connectivity maps in the optimal prediction of sensorimotor behavior. Second, the maximal association solution between the behavioral outcome and multimodal lesion connectivity maps suggests an equal contribution of sensory and motor coefficients, in contrast to the unimodal analyses where the sensory contribution dominates in both structural and functional maps. Finally, when looking at each modality individually, the performance of the structural connectivity maps strongly depends on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect of larger lesions that produce more severe sensorimotor dysfunction. By contrast, the maps of functional connectivity performed similarly irrespective of any correction for lesion size. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of imaging modalities, and the influence of their corresponding brain networks on behavioral performance after acquired brain injury.

neuroscience↗

bioRxiv2021

Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging

A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We developed a QSM processing pipeline to estimate magnetic susceptibility of multiple brain structures in 35,885 subjects from the UK Biobank prospective epidemiological study. We identified phenotypic associations of magnetic susceptibility that include body iron, disease, diet, and alcohol consumption. Genome-wide associations related magnetic susceptibility to genetic variants with biological functions involving iron, calcium, myelin, and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers world-wide, creating potential to discover novel, non-invasive markers of brain health.

neuroscience↗

bioRxiv2021

Multimodal brain features at preschool age and the relationship with pre-reading measures one year later: an exploratory study

Pre-reading language skills develop rapidly in early childhood and are related to brain structure and function in young children prior to formal education. However, the early neurobiological development that support these skills is not well understood and has not been assessed longitudinally using multiple imaging approaches. Here we acquired anatomical, diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) from 35 children at 3.5 years of age. Children were assessed for pre-reading abilities using the NEPSY-II subtests one year later (4.5 years). We applied a data-driven linked independent component analysis to explore the shared co-variation of grey and white matter measures. Two sources of structural variation at 3.5 years of age demonstrated a weak relationship with Speeded Naming scores at 4.5 years of age. The first imaging component involved volumetric variability in reading-related cortical regions alongside microstructural features of the superior longitudinal fasciculus. The second component was dominated by cortical volumetric variations within the cerebellum and visual association area. In a subset of children with rs-fMRI data, we evaluated the inter-network functional connectivity of the left-lateralized fronto-parietal language (FPL) network and its relationship with pre-reading measures. Higher functional connectivity between the FPL functional network and the default mode and visual networks at 3.5 years predicted better Phonological Processing scores at 4.5 years. Together, these results suggest that the integration of functional networks, as well as the co-development of white and grey matter brain structures in early childhood, may support the emergence of pre-reading measures in preschool children.

neuroscience↗

bioRxiv2021

Non-linearity matters: a deep learning solution to the generalization of hidden brain patterns across population cohorts

Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ( latent indices) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.

neuroscience↗

bioRxiv2020

Gray matter covariations and core symptoms of autism. The EU-AIMS Longitudinal European Autism Project

BackgroundVoxel-based Morphometry (VBM) studies in Autism Spectrum Disorder (autism) have yielded diverging results. This might partly be attributed to structural alterations being associating with the combined influence of several regions rather than with a single region. Further, these structural covariation differences may relate to continuous measures of autism rather than with categorical case-control contrasts. The current study aimed to identify structural covariation alterations in autism, and assessed canonical correlations between brain covariation patterns and core autism symptoms. MethodsWe studied 347 individuals with autism and 252 typically developing individuals, aged between 6 and 30 years, who have been deeply phenotyped in the Longitudinal European Autism Project (LEAP). All participants VBM maps were decomposed into spatially independent components using Independent Component Analysis. A Generalized Linear Model (GLM) was used to examine case-control differences. Next, Canonical Correlation Analysis (CCA) was performed to separately explore the integrated effects between all the brain sources of gray matter variation and two sets of core autism symptoms. ResultsGLM analyses showed significant case-control differences for two independent components. The first component was primarily associated with decreased density of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and increased density of caudate nucleus in the autism group relative to typically developing individuals. The second component was related to decreased densities of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to typically developing individuals. The CCA results showed significant correlations between components that involved variation of thalamus, putamen, precentral gyrus, frontal, parietal, and occipital lobes, and the cerebellum, and repetitive, rigid and stereotyped behaviors and abnormal sensory behaviors in autism individuals. LimitationsOnly 55.9% of the participants with autism had complete questionnaire data on continuous parent-reported symptom measures. ConclusionsCovaried areas associated with autism diagnosis and/or symptoms are scattered across the whole brain and include the limbic system, basal ganglia, thalamus, cerebellum, precentral gyrus, and parts of the frontal, parietal, and occipital lobes. Some of these areas potentially subserve social-communicative behavior whereas others may underpin sensory processing and integration, and motor behavior.

neuroscience↗