Alberto Llera

This study integrates brain structure and function to identify genetic loci associated with language-related brain components using UK Biobank data.

This paper introduces Independent Filter Analysis (IFA), a supervised method for group discrimination in fMRI that outperforms traditional ICA in detecting group differences.

This study investigates the neural correlates of dream recall frequency using multimodal neuroimaging in 258 individuals, finding associations between DRF and brain structure and function.

The paper uses multimodal MRI data to explore brain-behavior relationships, finding some components linked to intoxication and working memory but no significant shared variance between structural and functional organisation.

The paper maps functional connectivity patterns in the somatosensory cortex during rest and emotional tasks in ASD and neurotypical individuals, linking them to adaptive functioning skills.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.

This paper extends lesion network mapping to a multimodal approach using functional and structural connectivity maps to predict sensorimotor behavior in stroke patients.

The paper identifies a multimodal brain pattern associated with autism by integrating gray and white matter morphology data and linking it to clinical symptoms.

The paper develops a QSM pipeline to analyze brain magnetic susceptibility in UK Biobank data, finding associations with iron, disease, and genetic variants.

This paper investigates whether fMRI functional connectivity reflects dynamic neural communication by developing and applying PCCA to show context-dependent, cognitively relevant changes in connectivity trajectories.

This study examines how brain features in preschool children relate to pre-reading skills one year later using multimodal imaging and behavioral assessments.

The paper learns interpretable latent representations of task-fMRI data from large-scale datasets, capturing demographic and behavioral variability through a three-dimensional autoencoder and normative modeling.

The paper develops methods to assess time-varying functional connectivity in fMRI and shows it explains behavioural traits not captured by structural data or time-averaged connectivity.

The paper uses LICA to identify multivariate associations between gut microbiota and brain network connectivity in healthy females, finding specific microbial genera linked to brain network strength.

The paper identifies structural covariation alterations in autism using VBM and links these patterns to core autism symptoms through canonical correlation analysis.

The paper introduces a supervised dimensionality reduction model for fMRI data to discriminate between tasks and demonstrates its effectiveness using the Human Connectome Project dataset.

The paper links inter-individual differences in human brain structure to demographic and behavioral variations using structural MRI data from a large cohort.

The paper introduces a framework called task potency to measure task-specific functional connectivity modulations and applies it to compare three cognitive tasks.