Reading Group: Prediction of Alzheimer's disease in subjects with mild cognitive impairment

SpeakerLorna Harper
AffiliationUCL
DateThursday, 31 Oct 2013
Time16:00 - 17:30
LocationFoster Court 219
Event seriesMachine Learning for Neuroimaging Reading Group
Description

PAPER: "Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning", Eskildsen, et al.

Predicting Alzheimer's disease (AD) in individuals with some symptoms of cognitive decline may have great
influence on treatment choice and disease progression. Structural magnetic resonance imaging (MRI) has the
potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting
and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated
high sensitivity to cortical gray matter changes. In this study we investigated the possibility for using patterns of
cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI).We used a
novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI
who progress to probable AD, from individuals with MCI who do not progress to probable AD. Specific patterns
of atrophy were identified at four time periods before diagnosis of probable AD and features were selected as
regions of interest within these patterns. The selected regions were used for cortical thickness measurements
and applied in a classifier for testing the ability to predict AD at the four stages. In the validation, the test subjects
were excluded from the feature selection to obtain unbiased results. The accuracy of the prediction improved as
the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was
met, to 76% at 6 months before AD. By inclusion of test subjects in the feature selection process, the prediction
accuracies were artificially inflated to a range of 73% to 81%. Two important results emerge from this study.
First, prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that
are specific to the different stages of disease progression. This has the potential to guide the further development
of imaging biomarkers in AD. Second, the results show that one needs to be careful when designing training, testing
and validation schemes to ensure that datasets used to build the predictivemodels are not used in testing and
validation.

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