Artificial Intelligence Methods
Artificial Intelligence Methods
to Decode Emotions and Personality
to Decode Emotions and Personality
TASK FORCE
TASK FORCE
Embedded Files
The aim of this task force is to apply artificial intelligence methods to study how emotions can be decoded from brain features, and to improve classification of personality and clinical syndromes
The aim of this task force is to apply artificial intelligence methods to study how emotions can be decoded from brain features, and to improve classification of personality and clinical syndromes
Personnel
Personnel
Cristiano Crescentini - Department of Language, Literature and Communication, and Society, University of Udine, Italy
Cristiano Crescentini - Department of Language, Literature and Communication, and Society, University of Udine, Italy
Paola Feraco - Neuroradiology Unit, S. Chiara Hospital, APSS, Trento, Italy
Paola Feraco - Neuroradiology Unit, S. Chiara Hospital, APSS, Trento, Italy
Alessandro Grecucci - Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Science, & Center for Medical Sciences, University of Trento, Italy
Alessandro Grecucci - Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Science, & Center for Medical Sciences, University of Trento, Italy
Irene Messina - Department of Psychology and Cognitive Sciences, University of Trento & Psychology programme, Universitas Mercatorum, Rome, Italy
Irene Messina - Department of Psychology and Cognitive Sciences, University of Trento & Psychology programme, Universitas Mercatorum, Rome, Italy
Gerardo Salvato - Cognitive Neuropsychology Centre, ASST, Grande Ospedale Metropolitano Niguarda, Milano, & Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
Gerardo Salvato - Cognitive Neuropsychology Centre, ASST, Grande Ospedale Metropolitano Niguarda, Milano, & Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
Roma Siugzdaite - MRC Cognition and Brain Sciences Unit, Department of Psychology, University of Cambridge, UK
Roma Siugzdaite - MRC Cognition and Brain Sciences Unit, Department of Psychology, University of Cambridge, UK
Methods:
Methods:
Supervised and unsupervised machine learning is applied to strctural and functional MRI, Resting state-MRI, EEG, and behavioral data
Supervised and unsupervised machine learning is applied to strctural and functional MRI, Resting state-MRI, EEG, and behavioral data
Relevant publications
Relevant publications
Caria A., Grecucci, A. (under review) Predicting real-time fMRI based emotional brain regulation from structural MR data. An approach based on Kernel Ridge regression.
Caria A., Grecucci, A. (under review) Predicting real-time fMRI based emotional brain regulation from structural MR data. An approach based on Kernel Ridge regression.
Sorella, S., Vellani, V., Siugzdaite, R., Feraco, P., Grecucci, A. (under review) Structural and functional brain networks of individual differences in trait anger and anger control: an unsupervised machine learning study. European Journal of Neuroscience
Sorella, S., Vellani, V., Siugzdaite, R., Feraco, P., Grecucci, A. (under review) Structural and functional brain networks of individual differences in trait anger and anger control: an unsupervised machine learning study. European Journal of Neuroscience
Dadomo, H., Salvato, G., Lapomarda, G., Ciftci, Z., Messina, I., Grecucci, A. (under review). Structural features predict sexual trauma and interpersonal problems in Borderline personality disorder but not in controls: a Multi-voxel pattern analysis. Brain Research
Dadomo, H., Salvato, G., Lapomarda, G., Ciftci, Z., Messina, I., Grecucci, A. (under review). Structural features predict sexual trauma and interpersonal problems in Borderline personality disorder but not in controls: a Multi-voxel pattern analysis. Brain Research
Caria, A., Grecucci, A. (in press). Predicting real-time fMRI-based insula regulation from brain structure. Affective Sciences
Caria, A., Grecucci, A. (in press). Predicting real-time fMRI-based insula regulation from brain structure. Affective Sciences
Lapomarda, G., Grecucci, A., Messina, I., Pappaianni, E., Dadomo H. (2021). Common and different gray and white matter alterations in Bipolar and Borderline Personality Disorder. Brain Research, 1762:147401.
Lapomarda, G., Grecucci, A., Messina, I., Pappaianni, E., Dadomo H. (2021). Common and different gray and white matter alterations in Bipolar and Borderline Personality Disorder. Brain Research, 1762:147401.
Lapomarda, G., Pappaianni, E., Siugzdaite, R., Sanfey, A.G., Rumiati, R.I., Grecucci, A. (2021). Out of control: An altered parieto-occipital-cerebellar network for impulsivity in bipolar disorder. Behavioural Brain Research, 406:113228.
Lapomarda, G., Pappaianni, E., Siugzdaite, R., Sanfey, A.G., Rumiati, R.I., Grecucci, A. (2021). Out of control: An altered parieto-occipital-cerebellar network for impulsivity in bipolar disorder. Behavioural Brain Research, 406:113228.
Saviola, F., Pappaianni, E., Monti, A., Grecucci, A., Jovicich, J., De Pisapia, N. (2020). Trait and state anxiety are mapped differently in the human brain. Scientific reports, 10, 11112.
Saviola, F., Pappaianni, E., Monti, A., Grecucci, A., Jovicich, J., De Pisapia, N. (2020). Trait and state anxiety are mapped differently in the human brain. Scientific reports, 10, 11112.
Pappaianni, E., De Pisapia, N., Siugzdaite, R., Crescentini, C., Calcagnì, A., Job, R., Grecucci, A. (2019). Less is more: psychological and morphometric differences between low vs high reappraisers. Cognitive, Affective & Behavioral Neuroscience, 20, 128–140.
Pappaianni, E., De Pisapia, N., Siugzdaite, R., Crescentini, C., Calcagnì, A., Job, R., Grecucci, A. (2019). Less is more: psychological and morphometric differences between low vs high reappraisers. Cognitive, Affective & Behavioral Neuroscience, 20, 128–140.
Sorella, S., Lapomarda, G., Messina, I., Siugzdaite, R., Job, R., Grecucci, A. (2019). Testing the expanded continuum hypothesis of schizophrenia and bipolar disorder. Neural and psychological evidence for shared and distinct mechanisms. Neuroimage: Clinical. 23, 101854.
Sorella, S., Lapomarda, G., Messina, I., Siugzdaite, R., Job, R., Grecucci, A. (2019). Testing the expanded continuum hypothesis of schizophrenia and bipolar disorder. Neural and psychological evidence for shared and distinct mechanisms. Neuroimage: Clinical. 23, 101854.
Pappaianni, E., Siugzdaite, R. Vettori, S., Venuti, P., Job, R., Grecucci, A. (2018). Three shades of grey: detecting brain abnormalities in children with autism by using Source-, Voxel- and Surface-based Morphometry. European Journal of Neuroscience, 47(6), 690-700.
Pappaianni, E., Siugzdaite, R. Vettori, S., Venuti, P., Job, R., Grecucci, A. (2018). Three shades of grey: detecting brain abnormalities in children with autism by using Source-, Voxel- and Surface-based Morphometry. European Journal of Neuroscience, 47(6), 690-700.
Website administrator: Alessandro Grecucci
Website administrator: Alessandro Grecucci
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