Alejandro I de la Vega

Mining the neuroimaging literature

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

NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images

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

Advancing image-based meta-analysis for fMRI: A framework for leveraging NeuroVault data

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.

Meta all the way down: An overview of neuroimaging meta-analyses

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

Methods for decoding cortical gradients of functional connectivity

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

Neuroscout, a unified platform for generalizable and reproducible fMRI research

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

Analysis of task-based functional MRI data preprocessed with fMRIPrep

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

Heterogeneity Within the Frontoparietal Control Network and its Relationship to the Default and Dorsal Attention Networks

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.

Large-scale meta-analysis suggests low regional modularity in lateral frontal cortex.

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.