1 / 22

G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé ,

Development of a Binary fMRI-BCI for Alzheimer patients A s emantic conditioning paradigm using affective unconditioned stimuli. G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé , S. Kim, A. Raffone , M. Olivetti, N. Birbaumer , R. Sitaram

amy
Download Presentation

G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé ,

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Development of a Binary fMRI-BCI for Alzheimer patients A semantic conditioning paradigm using affective unconditioned stimuli G. Liberati, J. Dalboni, R. Veit, C. von Arnim, A. Jenner, D. Lulé, S. Kim, A. Raffone, M. Olivetti, N. Birbaumer, R. Sitaram giulia.liberati@uclouvain.be

  2. Passive BCIs

  3. Why use fMRI? • Recording of activity from whole brain • Recognition of patterns of spontaneous activity • Possibility to develop online classification

  4. Detecting mental states with fMRI Sitaram et al. (2012), Neuroimage

  5. Mental state classification with classical conditioning (CC) in healthy subjects • 10 healthy subjects (5 males, 5 females; age: 21-28) • 3T fMRI scanner • Conditioned stimuli (CS): 300 congruent and incongruent word-pairs • “Fruit – Apple”  congruent  “yes” thinking • “Fruit – Dog”  incongruent  “no” thinking • Unconditioned stimuli (US): sounds from IADS (Bradley & Lang 1999) • Baby-laugh  following congruent word-pairs • Scream  following incongruent word-pairs Van der Heiden, Liberati & al., submitted

  6. Mental state classification with CC in healthy subjects Van der Heiden, Liberati & al., submitted „Fruit-Apple“ „Fruit-Dog“  „Yes“ thinking  „No“ thinking & & US1 CS2 US2 CS1 Change in BOLD signal Change in BOLD signal Differentiable?

  7. Mental state discrimination with classical conditioning in healthy subjects • Insula activations for Incongruent > Congruent contrast during acquisition and extinction, but not during habituation • Conditioning with emotional stimuli took place Extinction: Right insula activation for CS1>CS2 Acquisition: Bilateral insula activation for CS1+>CS2+

  8. Mental state classification in Alzheimer’s disease (AD) • Information on basic thoughts of AD patients who have lost the ability to communicate verbally • Lack of research in this direction: BCI traditionally considered to require an intact cognitive system • Affectivity is usually more preserved in AD

  9. Subjects • 6 mild AD patients • 2 males, 4 females • Age: 69-91 • MMSE: 19-24 • 7 healthy controls • 5 males, 2 females • Age: 62-83

  10. Procedure

  11. Data analysis • Selection of the fMRI signals within each voxel of insula, amygdala and ACC • “Searchlight approach” (Kriegeskorte et al. 2006) • 4thand 5th volumes after the presentation of unpaired word-pairs • Linear Support Vector Machine (SVM) • Classification accuracy computed by averaging the classification accuracies from 35 replications of a leave-one-out cross-validation principle.

  12. Results - Patients Classification results selecting insula, amygdala and ACC (1000 voxels)

  13. Results – Control subjects Classification results selecting insula, amygdala and ACC (1000 voxels)

  14. Insula: Patients Classification results selecting insula, 100 voxels

  15. Insula: Control subjects Classification results selecting insula, 100 voxels

  16. Amygdala: Patients Classification results selecting amygdala, 50 voxels

  17. Amygdala: Control subjects Classification results selecting amygdala, 50 voxels

  18. ACC: Patients Classification results selecting ACC, 300 voxels

  19. ACC: Control subjects Classification results selecting ACC, 300 voxels

  20. Conclusion • Focusing on insula alone leads to better classification results compared to combining insula, ACC and amygdala together • Focusing on ACC or amygdala individually leads to a decrease of classification accuracy • When focusing on the insula, classification accuracy seems to be higher for AD patients compared to controls • Underlying different cognitive processes for the two groups?

  21. Discussion • We assessed a novel affective-BCI approach using CC with emotional stimuli in combination with brain state classification • Discrimination between affirmative and negative responses following CC is possible in AD patients, comparably to matched controls • “Passive” paradigm: low cognitive effort, CC • Typical obstacles of traditional BCIs, which generally require time-consuming trainings and intact cognition, are overcome

  22. Future directions • Implementation of an online SVM (real-time fMRI) • More portabledevices (NIRS) • Testing with otherkinds of patients (e.g. FTD)

More Related