James Doliver Kent

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.

fMRIPrep, a BIDS-driven, self-adapting fMRI preprocessing pipeline, earned above-acceptable expert quality ratings on 50 of 54 OpenfMRI datasets and introduced less smoothing than FSL feat.