Stefano Anzellotti
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steanze.bsky.social
Stefano Anzellotti
@steanze.bsky.social
16 followers 27 following 1 posts
Associate professor in Psychology and Neuroscience at Boston College.
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steanze.bsky.social
Some new work from the lab: @yuzhu194.bsky.social and Aidas Aglinskas introduce a deep-learning based fMRI denoising method that outperforms CompCor by over 200%.
yuzhu194.bsky.social yuzhu194.bsky.social @yuzhu194.bsky.social · 28d
Excited to share our new Nature Methods paper on DeepCor, a contrastive autoencoder for denoising fMRI. DeepCor separates neural signal from noise at the voxel level, works for single participants, and outperforms CompCor by up to 215% on real data. 1/7

doi.org/10.1038/s415...
DeepCor: denoising fMRI data with contrastive autoencoders - Nature Methods
DeepCor is a deep-learning-based denoising approach for task-based and resting-state fMRI data that can be used even for single participants.
doi.org
December 1, 2025 at 6:11 PM
Reposted by Stefano Anzellotti
gabefajardo.bsky.social
I’m excited to share my 1st first-authored paper, “Distinct portions of superior temporal sulcus combine auditory representations with different visual streams” (with @mtfang.bsky.social and @steanze.bsky.social ), now out in The Journal of Neuroscience!
www.jneurosci.org/content/earl...
Fig. 1. a. Visual and auditory regions of interest (ROIs). b. Responses in a combination of visual (e.g., early dorsal visual stream; Fig. 1a, middle panel) and auditory regions were used to predict responses in the rest of the brain using MVPN. c. In order to identify brain regions that combine responses from auditory and visual regions, we identified voxels where predictions generated using the combined patterns from auditory regions and one set of visual regions jointly (as shown in Fig. 1b) are significantly more accurate than predictions generated using only auditory regions or only that set of visual regions.
October 2, 2025 at 3:20 PM