slide1 n.
Download
Skip this Video
Download Presentation
G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé ,

Loading in 2 Seconds...

play fullscreen
1 / 22

G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé , - PowerPoint PPT Presentation


  • 118 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé ,' - amy


Download Now 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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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

why use fmri
Why use fMRI?
  • Recording of activity from whole brain
  • Recognition of patterns of spontaneous activity
  • Possibility to develop online classification
detecting mental states with fmri
Detecting mental states with fMRI

Sitaram et al. (2012), Neuroimage

mental state classification with classical conditioning cc in healthy subjects
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

mental state classification with cc in healthy subjects
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?

mental state discrimination with classical conditioning in healthy subjects
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+

m ental state classification in alzheimer s disease ad
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
subjects
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
data analysis
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.
results patients
Results - Patients

Classification results selecting insula, amygdala and ACC (1000 voxels)

results control subjects
Results – Control subjects

Classification results selecting insula, amygdala and ACC (1000 voxels)

insula patients
Insula: Patients

Classification results selecting insula, 100 voxels

insula control subjects
Insula: Control subjects

Classification results selecting insula, 100 voxels

amygdala patients
Amygdala: Patients

Classification results selecting amygdala, 50 voxels

amygdala control subjects
Amygdala: Control subjects

Classification results selecting amygdala, 50 voxels

acc patients
ACC: Patients

Classification results selecting ACC, 300 voxels

acc control subjects
ACC: Control subjects

Classification results selecting ACC, 300 voxels

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