An oblique approach to prediction of conversion to alzheimer’s disease with multikernel gaussian processes

TY - CHAP

T1 - An oblique approach to prediction of conversion to alzheimer’s disease with multikernel gaussian processes

AU - Young, Jonathan

AU - Modat, Marc

AU - Cardoso, Manuel J.

AU - Ashburner, John

AU - Ourselin, Sebastien

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Machine learning approaches have had some success in predicting conversion to Alzheimer’s Disease (AD) in subjects with mild cognitive impairment (MCI), a less serious condition that nonetheless is a risk factor for AD. Predicting conversion is clinically important as because novel drugs currently being developed require administration early in the disease process to be effective. Traditionally training data are labelled with discrete disease states; which may explain the limited accuracies obtained as labels are noisy due to the difficulty in providing a definitive diagnosis of Alzheimer’s without post-mortem confirmation, and ignore the existence of a continuous spectrum of disease severity. Here, we dispense with discrete training labels and instead predict the loss of brain volume over one year, a quantity that can be repeatably and objectively measured with the boundary shift integral and is strongly correlated with conversion. The method combines MRI and PET image data and cerebrospinal fluid biomarker levels in an Bayesian multi-kernel learning framework. The resulting predicted atrophy separates converting and non-converting MCI subjects with 74.6% accuracy, which compares well to state of the art methods despite a small training set size.

AB - Machine learning approaches have had some success in predicting conversion to Alzheimer’s Disease (AD) in subjects with mild cognitive impairment (MCI), a less serious condition that nonetheless is a risk factor for AD. Predicting conversion is clinically important as because novel drugs currently being developed require administration early in the disease process to be effective. Traditionally training data are labelled with discrete disease states; which may explain the limited accuracies obtained as labels are noisy due to the difficulty in providing a definitive diagnosis of Alzheimer’s without post-mortem confirmation, and ignore the existence of a continuous spectrum of disease severity. Here, we dispense with discrete training labels and instead predict the loss of brain volume over one year, a quantity that can be repeatably and objectively measured with the boundary shift integral and is strongly correlated with conversion. The method combines MRI and PET image data and cerebrospinal fluid biomarker levels in an Bayesian multi-kernel learning framework. The resulting predicted atrophy separates converting and non-converting MCI subjects with 74.6% accuracy, which compares well to state of the art methods despite a small training set size.

KW - Atrophy

KW - BSI

KW - Gaussian processes

KW - Mild cognitive impairment

KW - MRI

KW - Multi-kernel learning

KW - PET Alzheimer’s disease

KW - Regression

UR - http://www.scopus.com/inward/record.url?scp=84990026739&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-45174-9_13

DO - 10.1007/978-3-319-45174-9_13

M3 - Conference paper

AN - SCOPUS:84990026739

SN - 9783319451732

VL - 9444 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 122

EP - 128

BT - Machine Learning and Interpretation in Neuroimaging - 4th International Workshop, MLINI 2014 and Held at NIPS 2014, Revised Selected Papers

PB - Springer Verlag

T2 - 4th International Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2014 and workshop on Neural Information Processing Systems, NIPS 2014

Y2 - 13 December 2014 through 13 December 2014

ER -