Novel ThickNet features for the discrimination of amnestic MCI subtypes.
BACKGROUND
Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage between healthy aging and Alzheimer's disease (AD), and consists of two subtypes: single-domain aMCI (sd-aMCI) and multi-domain aMCI (md-aMCI). Individuals with md-aMCI are found to exhibit higher risk of conversion to AD. Accurate discrimination among aMCI subtypes (sd- or md-aMCI) and controls could assist in predicting future decline.


METHODS
We apply our novel thickness network (ThickNet) features to discriminate md-aMCI from healthy controls (NC). ThickNet features are extracted from the properties of a graph constructed from inter-regional co-variation of cortical thickness. We fuse these ThickNet features using multiple kernel learning to form a composite classifier. We apply the proposed ThickNet classifier to discriminate between md-aMCI and NC, sd-aMCI and NC and; and also between sd-aMCI and md-aMCI, using baseline T1 MR scans from the Sydney Memory and Ageing Study.


RESULTS
ThickNet classifier achieved an area under curve (AUC) of 0.74, with 70% sensitivity and 69% specificity in discriminating md-aMCI from healthy controls. The same classifier resulted in AUC = 0.67 and 0.67 for sd-aMCI/NC and sd-aMCI/md-aMCI classification experiments respectively.


CONCLUSIONS
The proposed ThickNet classifier demonstrated potential for discriminating md-aMCI from controls, and in discriminating sd-aMCI from md-aMCI, using cortical features from baseline MRI scan alone. Use of the proposed novel ThickNet features demonstrates significant improvements over previous experiments using cortical thickness alone. This result may offer the possibility of early detection of Alzheimer's disease via improved discrimination of aMCI subtypes.
School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, Canada.
Pradeep Reddy
PR
Raamana
Centre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW 2052, Australia ; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia.
Wei
W
Wen
Centre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW 2052, Australia ; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia.
Nicole A
NA
Kochan
Centre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW 2052, Australia ; Dementia Collaborative Research Centre, School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW 2052, Australia.
Henry
H
Brodaty
Centre for Healthy Brain Ageing, School of Psychiatry, UNSW Medicine, University of New South Wales, Sydney, NSW 2052, Australia ; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia.
Perminder S
PS
Sachdev
Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
Lei
L
Wang
School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, Canada.
Mirza Faisal
MF
Beg
D000368:Aged; D000369:Aged, 80 and over; D000647:Amnesia / Q000150:complications / Q000175:diagnosis*; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D060825:Cognitive Dysfunction / Q000150:complications / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests
Alzheimer; Cortical thickness; Early detection; Mild cognitive impairment; Network; ThickNet