• Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA 

  Reddy, Neha A.; Zvolanek, Kristina M.; Moia, Stefano; Caballero-Gaudes, César; Bright, Molly G. (MIT PRESS, 2024)

  Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson’s disease. However, motor-task fMRI is complicated by task-correlated head ...
• The CrowdGleason dataset: Learning the Gleason grade from crowds and experts 

  López-Pérez, Miguel; Morquecho, Alba; Schmidt, Arne; Pérez-Bueno, Fernando; Martín-Castro, Aurelio; Mateos, Javier; Molina, Rafael (ELSEVIER, 2024)

  Background: Currently, prostate cancer (PCa) diagnosis relies on the human analysis of prostate biopsy Whole Slide Images (WSIs) using the Gleason score. Since this process is error-prone and time-consuming, recent advances ...
• A novel image data base for social concepts reveals preference biases in autistic spectrum in adults and children 

  Soto, David ; Salazar, Amaia; Elosegi, Patxi; Walter, Antje; Mei, Ning; Rodriguez, Ekaine; Petrollini, Valentina; Vicente, Agustín (SPRINGER NATURE, 2024)

  Human beings display the extraordinary ability of grasping and communicating abstract concepts. Yet, no standardized instruments exist to assess this ability. Developing these tools is paramount for understanding abstract ...
• Sensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data 

  Kapralov, Nikolai; Idaji, Mina Jamshidi; Stephani, Tilman; Studenova, Alina; Vidaurre, Carmen; Ros, Tomas; Villringer, Arno; Nikulim, Vadim (IOP Publishing, 2024)

  Objective. Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain–computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user’s brain. ...

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