• Zhu*, Y., Aglinskas*, A. & Anzellotti., S. DeepCor: Denoising fMRI Data with Contrastive Autoencoders. (2025). Nature Methods. [link] *co-first authors

"Altmetric has tracked 30,043,550 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric." Altmetric.com

• Aglinskas, A., Bergeron A. & Anzellotti., S. Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity. (2025). Psychiatry and clinical neurosciences. [link]

• Aglinskas, A., & Anzellotti S. (2025). Improving action classification with brain-inspired deep networks. Arxiv

• Fang, M., Aglinskas, A., Li, Y., & Anzellotti, S. Angular gyrus responses show joint statistical dependence with brain regions selective for different categories. (2023). Journal of Neuroscience. [link]

• Aglinskas, A., Schwartz, E., & Anzellotti, S. (2023). Disentangling disorder-specific variation is key for precision psychiatry in autism. Frontiers in Behavioral Neuroscience. [link]

• Aglinskas, A., & Anzellotti, S. Precision psychiatry requires disentangling disorder‐specific variation: The case of ASD. (2022). Clinical and Translational Medicine. [link]

• Aglinskas, A., & Fairhall, S. L. Similar representation of names and faces in the network for person perception. (2023). NeuroImage. [link]

• Aglinskas, A., Hartshorne J.K. & Anzellotti, S.  (2022). Contrastive AI reveals the structure of individual variation in Autism Spectrum Disorder. Science. [link]. 

"Compared to other publications in the same field, this publication is extremely highly cited and has received approximately 32 times more citations than average"  Dimensions.ai

"Altmetric has tracked 27,524,720 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric." Altmetric.com

• Poskanzer, C., Fang, M., Aglinskas, A., & Anzellotti, S. (2021). Controlling for Spurious Nonlinear Dependence in Connectivity Analyses. Neuroinformatics, 1-13. [link]

• Liuzzi, A.G., Aglinskas, A., & Fairhall, S., L. (2020). General and feature-based semantic representations in the semantic network. Scientific Reports. [pdf]

"Compared to other publications in the same field, this publication is extremely highly cited and has received approximately 10 times more citations than average"  Dimensions.ai

• Fang, M., Aglinskas, A., Li, Y., & Anzellotti, S. (2019). Artificial neural networks reveal multivariate integration of information from multiple category-selective regions. PsyArXiv. [pdf]

• Aglinskas, A., & Fairhall, S. L. (2019). Regional Specialization and Coordination Within the Network for Perceiving and Knowing About Others. Cerebral Cortex. [pdf]

• Aglinskas, A. (2019). Network Level Representation of Conceptual Content (Doctoral dissertation, University of Trento). [link] [pdf]

• Gardner, T., Aglinskas, A., & Cross, E. S. (2017). Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity. NeuroImage, 156, 174-189. [pdf]

• Aglinskas, A. (2015). Modulatory plasticity of the action observation network by action familiarity. MSc Thesis, Bangor University. [pdf]