Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning

• Published in: NeuroImage (Orlando, Fla.) Vol. 65; pp. 511 - 521
• Main Authors: Eskildsen, Simon F., Coupé, Pierrick, García-Lorenzo, Daniel, Fonov, Vladimir, Pruessner, Jens C., Collins, D. Louis
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15.01.2013; Elsevier; Elsevier Limited
• Subjects: Accuracy; Adult and adolescent clinical studies; Algorithms; Alzheimer Disease - diagnosis; Atrophy - pathology; Bioengineering; Biological and medical sciences; Brain - pathology; Classification; Cognitive Dysfunction - pathology; Computer Science; Confidence intervals; Cortical thickness; Cost reduction; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Disease Progression; Early Diagnosis; Engineering Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing; Life Sciences; Magnetic Resonance Imaging - methods; MCI; Medical Imaging; Medical sciences; MRI; Neurodegeneration; Neurology; Neurons and Cognition; Organic mental disorders. Neuropsychology; Prediction; Principal components analysis; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Signal and Image processing; Studies; MCI; AD; Cortical thickness; MRI; Prediction; Human; Nervous system diseases; Alzheimer disease; Nuclear magnetic resonance imaging; Cerebral disorder; Central nervous system disease; Degenerative disease; mild cognitive impairment
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.09.058

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 3years before the clinical criteria for AD was met, to 76% at 6months 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 predictive models are not used in testing and validation. ► Novel approach for subgrouping MCI subjects with respect to “time to conversion” ► New method for automatically selecting highly discriminative cortical features ► Cortical thickness extracted on all ADNI data with very small exclusion rate (2.7%) ► Patterns of cortical thinning in agreement with histological findings (Braak stages) ► Superior accuracy for predicting conversion to Alzheimer's disease depending on the time to conversion (70%–76%)