Author

Patrizia Fattori

University of BolognaORCID 0000-0002-0079-3755Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Research Focus:dimensionality reduction· 2 Organism:mouse· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Stefano DiomediiD· 3 shared Francesco Edoardo VaccariiD· 2 shared Ivilin Peev StoianoviD· 2 shared Kostas HadjidimitrakisiD· 2 shared Matteo FilippiniiD· 2 shared Antonio Roberto BuonfiglioiD· 1 shared Edoardo BettazziiD· 1 shared Marina De VitisiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 22 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersNeuroscience

Stefano DiomediORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Integration of Motor Planning and Execution through Latent Structure Reorganization in the Posterior Parietal Cortex

Stefano Diomedi, Francesco Edoardo Vaccari, Kostas Hadjidimitrakis, Patrizia Fattori, Ivilin Peev Stoianov

The paper analyzes neural dynamics in PPC areas during motor tasks, finding distinct population subspaces for planning and execution in different regions.

bioRxiv · Jun 10, 2025 · PreprintNeuroscience

Dynamic Predictive Spatial Encoding of Motor Intentions In Area V6A of the Posterior Parietal Cortex

Antonio Roberto Buonfiglio, Stefano Diomedi, Matteo Filippini, Patrizia Fattori, Ivilin Peev Stoianov

This study investigates how the brain encodes spatial intentions in area V6A during motor planning, revealing dynamic predictive encoding mechanisms.

bioRxiv · Feb 6, 2025 · PreprintNeuroscience

More or fewer latent variables in the high-dimensional data space? That is the question

Francesco Edoardo Vaccari, Stefano Diomedi, Edoardo Bettazzi, Matteo Filippini, Marina De Vitis, Kostas Hadjidimitrakis, Patrizia Fattori

This study evaluates methods for determining the optimal number of latent variables in high-dimensional neural data, finding that parallel analysis and cross-validation are most effective.

bioRxiv · Nov 28, 2024 · PreprintNeuroscience