Differential diagnosis of neurodegenerative diseases using structural MRI data

Abstract

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.

Keywords: Alzheimer's disease; Classification; Dementia with Lewy bodies; Frontotemporal lobar degeneration; MRI; Neurodegenerative diseases; TBM; VBM; Vascular dementia; Volumetry.

Graphical abstract

Fig. B.8

Visualization of the computation of the fitness value. Upper figure shows the probability distributions for the state 0 and the state 1, and lower figure shows the curve for the fitness value. The data shown here are for the volume of right hippocampus where the state 0 is the CN group and the state 1 is the FTD group. The dashed line shows an example for a patient with the right hippocampus volume of 1750 mm³. This feature value fits better to the distribution of state 1 resulting in high fitness value.

Fig. 1

An example of the segmentations of T1 MR image.

Fig. 2

ROIs used for manifold learning and ROI-based grading: red = hippocampus region, blue = frontotemporal lobe region, purple = ROIs overlapping. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

An example of segmentation of a FLAIR image.

Fig. 4

Examples of pair-wise t-maps for TBM. Red = smaller local volume in latter group, blue = larger local volume in latter group. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Examples of pair-wise t-maps for VBM. Red = smaller local GM concentration in latter group, blue = larger local GM concentration in latter group. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Examples of correctly classified patients with high likelihood.

Fig. 7

Examples of misclassified patients.