Differentiating Alzheimer's Disease from Dementia with Lewy Bodies Using a Deep Learning Technique Based on Structural Brain Connectivity

Abstract

Purpose: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are representative disorders of dementia of the elderly and the neuroimaging has contributed to early diagnosis by estimation of alterations of brain volume, blood flow and metabolism. A brain network analysis by MR imaging (MR connectome) is a recently developed technique and can estimate the dysfunction of the brain network in AD and DLB. A graph theory which is a major technique of network analysis is useful for a group study to extract the feature of disorders, but is not necessarily suitable for the disorder differentiation at the individual level. In this investigation, we propose a deep learning technique as an alternative method of the graph analysis for recognition and classification of AD and DLB at the individual subject level.

Materials and methods: Forty-eight brain structural connectivity data of 18 AD, 8 DLB and 22 healthy controls were applied to the machine learning consisting of a six-layer convolution neural network (CNN) model. Estimation of the deep learning model to classify AD, DLB and non-AD/DLB was performed using the 4-fold cross-validation method.

Results: The accuracy, average precision and recall of our CNN model were 0.73, 0.78 and 0.73, and the specificity precision and recall were 0.68 and 0.79 in AD, 0.94 and 0.65 in DLB and 0.73 and 0.75 in non-AD/DLB. The triangular probability map of the MR connectome revealed the probability of AD, DLB and non-AD/DLB in each subject.

Conclusion: Our preliminary investigation revealed the adaptation of deep learning to the MR connectome and proposed its utility in the differentiation of dementia disorders at the individual subject level.

Keywords: Alzheimer’s disease; deep learning; dementia with Lewy bodies; structural brain connectivity.

Fig. 1

The principle of the deep learning MR connectome. The connection between brain areas is expressed by a network graph consisting of nodes and edges. The “network graph” can be converted to an adjacent matrix and calculated by a graph theory. The adjacent matrix is similar to the image data and it can be an input of convolution neural network (CNN) model. The deep learning of MR connectome outputs the probability map which estimates the probability of AD, DLB and non-AD/DLB with a triangular graph. AD, Alzheimer’s disease; DLB, dementia with Lewy bodies.

Fig. 2

A convolution neural network (CNN) machine learning model adapted to the MR connectome. As a machine learning model, a six-layer CNN model with three convolution layers (each consisting of convolution, ReLU; Rectified Linear Unit and MaxPooling) and three fully connected layers (Affine with ReLU) was adapted. The specific elements of the layer were described on the right side of each layer.

Fig. 3

The 4-fold cross-validation method. In the 4-fold cross-validation method, all sample data were split into four groups. One group was set as the test data and the remaining three groups were set as the training and validation data. An average of four times of investigations was estimated as the performance of the machine learning model.

Fig. 4

Probability map provided by the deep learning MR connectome of AD (A), DLB (B) and HC (C). The triangular radar graph reveals the probability of AD, DLB and non-AD/DLB in each subject ranging from 0.0 to 1.0. AD, Alzheimer’s disease; DLB, dementia with Lewy bodies; HC, healthy control.