LITcoder: A General-Purpose Library for Building and Comparing Encoding Models

2025-09-11

AI Summary

Neural encoding modeling faces challenges in stimulus–brain response alignment, feature mapping, and standardized cross-model/dataset comparison. To address these, we introduce NeuroEncode: an open-source, modular framework for developing and evaluating neural encoding models. NeuroEncode unifies temporal alignment of continuous stimuli (e.g., text, speech), hemodynamic delay correction, multi-level feature extraction—including deep neural networks and controlled baselines—region-specific fMRI response prediction, and rigorous information-leakage control. It integrates fMRI preprocessing, flexible region-of-interest selection, downsampling strategies, and Weights & Biases (W&B) experiment tracking, alongside built-in visualization and performance evaluation modules. Validated on three story-listening fMRI datasets, NeuroEncode substantially lowers the barrier to entry for neural encoding research, enhances methodological rigor, and enables systematic, reproducible model comparison—establishing a general-purpose infrastructure for high-performance, reproducible brain activity prediction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provides standardized tools for aligning continuous stimuli (e.g., text and speech) with brain data, transforming stimuli into representational features, mapping those features onto brain data, and evaluating the predictive performance of the resulting model on held-out data. The library implements a modular pipeline covering a wide array of methodological design choices, so researchers can easily compose, compare, and extend encoding models without reinventing core infrastructure. Such choices include brain datasets, brain regions, stimulus feature (both neural-net-based and control, such as word rate), downsampling approaches, and many others. In addition, the library provides built-in logging, plotting, and seamless integration with experiment tracking platforms such as Weights & Biases (W&B). We demonstrate the scalability and versatility of our framework by fitting a range of encoding models to three story listening datasets: LeBel et al. (2023), Narratives, and Little Prince. We also explore the methodological choices critical for building encoding models for continuous fMRI data, illustrating the importance of accounting for all tokens in a TR scan (as opposed to just taking the last one, even when contextualized), incorporating hemodynamic lag effects, using train-test splits that minimize information leakage, and accounting for head motion effects on encoding model predictivity. Overall, LITcoder lowers technical barriers to encoding model implementation, facilitates systematic comparisons across models and datasets, fosters methodological rigor, and accelerates the development of high-quality high-performance predictive models of brain activity. Project page: https://litcoder-brain.github.io

Problem

• Standardizing tools for aligning stimuli with brain data
• Implementing modular pipeline for encoding model composition
• Facilitating systematic comparisons across models and datasets

Innovation

• Open-source library for neural encoding models
• Modular pipeline for diverse methodological choices
• Built-in logging and integration with tracking platforms