Alejandro I de la Vega

The paper provides tools for collecting, processing, and annotating biomedical literature to streamline text-mining and meta-science projects.

NiCLIP predicts cognitive tasks and domains from brain activation maps using a contrastive language-image model trained on neuroscientific articles.

This paper develops a framework for image-based meta-analysis using NeuroVault data, showing that manual selection provides precise results while heuristic methods are effective for heterogeneous domains.

This paper reviews neuroimaging meta-analyses, finding that most follow PRISMA guidelines but have issues with pre-registration, search strategies, and study numbers.

This paper develops and evaluates methods for decoding functional connectivity gradients in fMRI data using meta-analytic techniques and k-means segmentation.

Neuroscout provides an automated platform for analyzing naturalistic fMRI data, enabling reproducible and generalizable research through machine learning and open tools.

This paper introduces fMRIPrep, a tool for preprocessing task-based fMRI data, and demonstrates its integration into a workflow for statistical analysis.

The paper identifies two distinct subsystems within the frontoparietal control network (FPCN) that show different functional connectivity patterns with the default and dorsal attention networks across multiple conditions.

This study performs a large-scale meta-analysis of fMRI data to map functional-anatomical regions in the lateral frontal cortex, finding low regional modularity and suggesting distributed network processing.