Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

Abstract

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

Fig. 1

Definitions of DBSCAN.

Fig. 2

Visualizing the different classification paradigms. Left: Support Vector Machine, Center: Bayesian Classification, Right: Voting Feature Intervals.

Fig. 3

Selected features for the comparison between AD vs. HC. z-coordinates in Talairach space: top row of images − 45.5, − 33.5, − 26.5, − 18.5, − 13.5, − 11.5, − 5.5; bottom row: − 3.5, 0.5, 4.5, 8.5, 13.5, 15.5, 21.5.

Fig. 4

(a) Cluster size and maximum Information Gain AD vs. HC. (b) Selected features after HC vs. AD clustering. Colors: Cluster 1 red, cluster 2 green, cluster 3: blue, cluster 4 purple cluster 5 orange. Remaining clusters gray. Displayed is every second slice starting with z = − 31.5 to 22.5; 34.5 and 35.5.

Fig. 5

(a) Cluster size and maximum Information Gain for MCI converter vs. MCI non-converter. (b) Skyline clusters of MCI-AD vs. MCI-MCI. Colors: cluster 1: red, cluster 2: green, cluster 3: blue, cluster 4: purple, cluster 5 orange. Displayed are some representative slices containing clusters: z-coordinates in Talairach space: − 12.5 to 5.5 and 34.5 to 42.5 (every second slice).

Fig. 6

Effect of the parameter C on the classification accuracy of SVM in task 1 (a) and task 4 (b). For both tasks we can observe only minor influence of C for very small C ( log₁₀(C) < − 3).