Development of a Binary fMRI-BCI for Alzheimer patients
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G. Liberati , J. Dalboni , R. Veit , C. von Arnim , A. Jenner, D. Lulé , - PowerPoint PPT Presentation


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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

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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

[email protected]



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
M healthy subjectsental 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 healthy 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


Procedure
Procedure healthy subjects


Data analysis
Data analysis healthy subjects

  • 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 healthy subjects

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


Results control subjects
Results – Control subjects healthy subjects

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


Insula patients
Insula: healthy subjectsPatients

Classification results selecting insula, 100 voxels


Insula control subjects
Insula: Control subjects healthy subjects

Classification results selecting insula, 100 voxels


Amygdala patients
Amygdala: healthy subjectsPatients

Classification results selecting amygdala, 50 voxels


Amygdala control subjects
Amygdala: Control subjects healthy subjects

Classification results selecting amygdala, 50 voxels


Acc patients
ACC: healthy subjectsPatients

Classification results selecting ACC, 300 voxels


Acc control subjects
ACC: Control subjects healthy subjects

Classification results selecting ACC, 300 voxels


Conclusion
Conclusion healthy subjects

  • 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 healthy subjects

  • 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 healthy subjects

  • Implementation of an online SVM (real-time fMRI)

  • More portabledevices (NIRS)

  • Testing with otherkinds of patients (e.g. FTD)


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