Towards a novel way to predict deficits after a brain lesion: a stroke exampleTowards a novel way to predict deficits after a brain lesion: a stroke example Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2020-04-222019enMany studies have addressed the relations between different human brain regions and their role in cognitive, motor and sensory functions in patients that have suffered a brain lesion (stroke, traumatic brain injury, tissue removal). Nowadays, it is well established that the brain works as a network and the symptoms in a person are a combination of the direct impact of the lesion in a single region and its connectivity with other healthy brain regions. The aim of the present study is the development of a user-friendly desktop application to predict the induced cognitive deficits in patients who have suffered a brain lesion. The herein presented application is based on Neurosynth platform, and takes as an input a MRI mask that describes a lesion. Then our software exploits the knowledge that already exists in Neurosynth platform, so as to predict the potential deficits by grouping the Neurosynth's terms that have increased Z scores with our mask. In addition, we have embedded two types of visualization methods: One to present the slices of the brain mask and another to show the 3D volume of the mask into 3D semitransparent human brain. The added value of the presented application is that it may give us a clue about which mechanisms are probably affected by a lesion in a specific region, while in the future it could provide neurosurgeons with insightful knowledge helping them in the plannification of a forthcoming surgical procedure. The proposed software was tested on 7 stroke patients, predicting accurately the 91% of the measured deficits found during a neuropsychological assessment.http://creativecommons.org/licenses/by/4.0/737-74119th International Conference on Bioinformatics and Bioengineering Klados Georgios Κλαδος Γεωργιος Zervakis Michail Ζερβακης Μιχαηλ Dacosta-Aguayo Rosalia Fratini Antonio Klados Manousos A. Institute of Electrical and Electronics Engineers Deficit prediction Lesion MRI Stroke Visualization