Clinical Biomarker Identification
Our Clinical Biomarker Identification Framework
Brain disorders affect millions worldwide, presenting intricate challenges in diagnosis, prognosis, and treatment. Our proficiency in explainable machine learning offers a unique opportunity to discover crucial neurophysiological biomarkers, guiding disorder management.
Comprehensive Preprocessing
In-Depth Biomarker Extraction
In-Depth Biomarker Extraction
We meticulously prepare brain data, removing artifacts that might prevent biomarker identification.
In-Depth Biomarker Extraction
In-Depth Biomarker Extraction
In-Depth Biomarker Extraction
We leverage advanced signal processing techniques, extracting a plethora of multimodal brain biomarkers.
Cutting-Edge Machine Learning and Deep Learning
Cutting-Edge Machine Learning and Deep Learning
Cutting-Edge Machine Learning and Deep Learning
We harness machine learning and deep learning techniques uniquely adapted to brain biomarkers.
Explainable Model Development
Cutting-Edge Machine Learning and Deep Learning
Cutting-Edge Machine Learning and Deep Learning
We integrate cutting-edge explainability methods, identifying biomarkers crucial to disorder management.
Example Publications
- Sendi, M.S.E., Fu, Z., Harnett, N.G. et al. (2025). Brain dynamics reflecting an intra-network brain state are associated with increased post-traumatic stress symptoms in the early aftermath of trauma. Nat. Mental Health 3, 185–198.
- Sendi M.S.E., (2025) Dry EEG—From Laboratory to Living Room. JAMA Psychiatry, 82(7):742–743. doi:10.1001/jamapsychiatry.2025.1073.
- Sendi, M.S.E., Zendehrouh, E., Miller, R.L., Fu, Z., Du, Y., Liu, J., Mormino, E.C., Salat, D.H., & Calhoun, V.D. (2021). Alzheimer's disease projection from normal to mild dementia reflected in functional network connectivity: A longitudinal study. Frontiers in Neural Circuits, 14, 593263. DOI: 10.3389/fncir.2020.593263.
- Noble. N. C, Sendi. M. S. E, Merker. J. B, Linton. S. R, Webber. T. K, Toll. R. T, Etkin. A, Wu. W, Ressler. K. J, Seligowski. A. V. (2025). PTSD-related differences in neural connectivity among female trauma survivors. Biological Psychiatry Global Open Science, 100491.
- Peddi. A, Sendi. M. S. E, Minton. S. T, Langhinrichsen-Rohling. R, Hinojosa. C. A, West. E, Ressler. K. J, Calhoun. V. D, van Rooij. S. J. H. (2025). Towards predicting posttraumatic stress symptom severity using portable EEG-derived biomarkers. Scientific Reports, 15(1), 5344.
- Ellis, C. A., Sattiraju, A., Miller, R. L., and Calhoun, V. D. (2022). Examining Effects of Schizophrenia on EEG with Explainable Deep Learning Models. in 22nd IEEE International Conference on BioInformatics and BioEngineering (IEEE). doi:10.1109/BIBE55377.2022.00068.
- Ellis, C.A., Sancho, M.L., Miller, R.L., Calhoun, V.D. (2024). Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2156. Springer, Cham. https://doi.org/10.1007/978-3-031-63803-9_6
- Ellis, C. A., Miller, R. L., and Calhoun, V. D. (2024). Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Front. Psychiatry 15, 1–13. doi:10.3389/fpsyt.2024.1165424
- Ellis, C. A., Miller, R. L., and Calhoun, V. D. (2023). A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis. in 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Sydney, Australia: IEEE), 1–4. doi:10.1109/EMBC40787.2023.10340375.
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