Jonathan Gallego Rudolf Alex Wiesman Sylvain Baillet Sylvia Villeneuve and the PREVENTAD Research Group Contact information jonathangallegorudolfmailmcgillca Background Can neurophysiological activity features extracted from rsMEG predict progression from the asymptomatic to the MCI ID: 1046916
Download Presentation The PPT/PDF document "Neurophysiological activity linked to AD..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
1. Neurophysiological activity linked to AD pathology relates to MCI progressionJonathan Gallego Rudolf, Alex Wiesman, Sylvain Baillet, Sylvia Villeneuve, and the PREVENT-AD Research Group Contact information: jonathan.gallegorudolf@mail.mcgill.caBackgroundCan neurophysiological activity features extracted from rs-MEG predict progression from the asymptomatic to the MCI stage of AD?Study aimAβ PETNeurophysiological activity (MEG)The synergistic effects of pathological Aβ and tau accumulation disrupt neurophysiological activityAβTauTau PET?
2. Participants - PREVENT-AD cohort103 participants with family history of sporadic AD (all cognitively unimpaired at enrolment).MethodsLongitudinal cognitive follow-ups20.4% MCI (mean time for progression 2.9 years).
3. Participants - PREVENT-AD cohort103 participants with family history of sporadic AD (all cognitively unimpaired at enrolment).MethodsLongitudinal cognitive follow-ups20.4% MCI (mean time for progression 2.9 years).Multimodal neuroimaging
4. Logistic regression model comparisonMCI progressors (n=21)Non-progressors (n=82)Models for predicting MCI progressionMethods
5. ResultsBasic clinical and MRI features provide moderate accuracy for predicting MCI progression( Clin = MRI)
6. Results( Clin = MRI > PET )The addition of Aβ and tau considerably increase the accuracy of the model
7. Neurophysiological features match the accuracy provided by Aβ and tau PET Results( Clin = MRI > MEG = PET > All )
8. ResultsNeurophysiological activity features outperformed the clinical and structural MRI models, matching the accuracy of Aβ and Tau PET for predicting MCI progression.MEG (and possibly EEG) could potentially serve as a screening strategy to select individuals for specialized invasive testing for AD pathology.( Clin = MRI > MEG = PET > All )Take home message
9. ResultsVisit my poster on Monday! P2-07–Biomarkers: Neuroimaging # P2-450( Clin = MRI > MEG = PET > All )
10. Take home and acknowledgementsPI: Dr. Sylvia VilleneuvePI: Dr. Sylvain Baillet