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Predictive brain imaging of hallucinations| old_uid | 14603 |
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| title | Predictive brain imaging of hallucinations |
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| start_date | 2014/11/07 |
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| schedule | 13h |
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| online | no |
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| location_info | salle 01-02 |
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| details | Conférence de l’Axe IV. Hosted by Philippe Domenech |
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| summary | Hallucinations are complex and transient mental states associated with subtle and possibly brain-wide patterns of activity that are not reliably detected at the subject-level using conventional approaches. Linear support-vector machine classification (lSVM) can robustly learn individual spatial patterns of fMRI activity, while maintaining excellent generalization performances. Here, we used lSVM to build a decision function that accurately predicts whether an hallucination occur or not (H+/H-) during scanning and acquired fMRI data from schizophrenia patients (DSM-IV-TR) with frequent hallucinations to benchmark candidate classifiers. lSVM achieved a 71% accuracy in separating H+/H- periods, and improved to 0.81 sensitivity and 0.75 specificity (?2=4.9; p<.02) once trained to neglect speech imagery (normal inner speech). We expect to further improve these classification accuracies by taking into account more complex hallucinatory experiences and non-hallucinated resting-state data in SCZ patients. |
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| responsibles | Oliviero |
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