Matthew P. Gunn, PhD

Computational neuroscientist and systems engineer with specializing in neuroimaging (EEG/fMRI), multimodal machine learning, and AI-driven analytics.

Matthew has led the design of reproducible data pipelines, autonomous software systems, and explainable AI frameworks across academic, startup, and applied research settings. His work integrates large-scale neural, behavioral, and genetic data to model latent brain states, reward processing, and psychopathology, with a focus on translation to precision mental health and neurotechnology.

He has served as a Postdoctoral Associate at the University of Illinois Chicago and has authored peer-reviewed publications, open-source tools used by 700+ researchers worldwide, and technical frameworks for reliable AI-assisted development.

Selected Work & Scholarship

Selected Publications

  • Gunn, M. P., Rose, G. M., Whitton, A. E., Pizzagalli, D. A., & Gilbert, D. G. (2024). Smoking progression and nicotine-enhanced reward sensitivity predicted by resting-state functional connectivity in salience and executive control networks. Nicotine & Tobacco Research, 26(10), 1305–1312. https://doi.org/10.1093/ntr/nta123

  • Anderson, Z., Gunn, M., Jones, E., Ajilore, O., Phan, K. L., de Wit, H., Klumpp, H., Calhoun, V., & Crane, N. A. (2025). Δ9-Tetrahydrocannabinol Alters Limbic and Frontal Functional Brain Connectomes Among Young Adult Cannabis Users. Biological psychiatry. Cognitive neuroscience and neuroimaging, S2451-9022(25)00282-4. Advance online publication. https://doi.org/10.1016/j.bpsc.2025.09.005

Open-Source Systems

  • NeuroLode (EEGLAB plugin v1.7.0) — Export to Excel/DAT/TXT/ASC; spectral features (centroid, kurtosis, skewness, spread) and modified BSS UI

  • eeg_preprocess —MATLAB + EEGLAB preprocessing template for EEG datasets. Automates channel selection, filtering, ASR sweeps, ICA, ICLabel rejection, and QC logging.

  • Spatiotemporal PCA (Data-Driven EEG Decomposition) — MATLAB + EEGLAB pipeline for temporal–spatial factorization of ERP data with parallel analysis, rotation options, and reproducible logging

  • NeuroToolbox — Generalizable Python package for data-driven neuroimaging analysis. Implements asymmetry indices, residual- and GPR-based normative modeling, multivariate PLS/CCA, network metrics, and robust regressions. Designed for reproducibility, with CLI/YAML workflows and CSV spreadsheet input for easy integration across labs and datasets

Technical Writing

Full CV available upon request.

Rigor before results.

We prioritize robustness, interpretability, and generalization over impressive but fragile outcomes.

Operating Principles

Systems over scripts.

We build reusable, auditable systems—not one-off analyses.

Inference over correlation.

Patterns are not explanations. We emphasize principled inference and causal sensitivity over surface-level associations.

Discretion and restraint.

Not all work is public-facing. Platforms, metrics, and partnerships are disclosed selectively to preserve integrity, privacy, and long-term optionality.

Reproducibility as a requirement.

Outputs must be traceable, repeatable, and reviewable. Reproducibility is an engineering constraint, not an afterthought.

Long-term alignment.

We evaluate decisions not only by immediate performance, but by how well they support durable systems, future extensions, and downstream trust.