Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network

Abstract

Purpose: Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning network on diffusion tractography connectome can accurately predict expressive and receptive language scores of children with epilepsy.

Methods: We studied 37 children with a diagnosis of drug-resistant focal epilepsy (age: 11.8 ± 3.1 years) using 3 T MRI and diffusion tractography connectome: G = (S, Ω), where S is an adjacency matrix of edges representing the connectivity strength (number of white-matter tract streamlines) between each pair of brain regions, and Ω reflects a set of brain regions. A convolutional neural network (CNN) was trained to learn the nonlinear relationship between 'S (input)' and 'language score (output)'. Repeated hold-out validation was then employed to measure the Pearson correlation and mean absolute error (MAE) between CNN-predicted and actual language scores.

Results: We found that CNN-predicted and actual scores were significantly correlated (i.e., Pearson's R/p-value: 0.82/<0.001 and 0.75/<0.001), yielding MAE: 7.77 and 7.40 for expressive and receptive scores, respectively. Specifically, sparse connectivity not only within the left cortico-cortical network but also involving the right subcortical structures was predictive of language impairment of expressive or receptive domain. Subsequent subgroup analyses inferred that the effectiveness of diffusion tractography-based prediction of language outcome was independent of clinical variables. Intrinsic diffusion tractography connectome properties may be useful for predicting the severity of baseline language dysfunction and possibly provide a better understanding of the biological mechanisms of epilepsy-related language impairment in children.

Keywords: Deep learning network; Diffusion-weighted imaging (DWI) tractography; Language prediction; Pediatric epilepsy.

Figure 1.

Schematic representation of the proposed convolutional neural network (CNN) architecture, where each colored square represents a specific network layer. Brain connectome matrix, ${\overline{S}}_l$ of the i^th patient is entered at the first stage, which is composed of two types of layers: convolution layer and batch normalization (BN) layer. The response of this stage is passed through a rectified linear unit (ReLU) layer. Then, the maximums of local patches are extracted by a max pool layer. Four blocks of convolution, BN, and ReLU layers are applied to learn high-level fine features from low-level features of the connectome matrix ${\overline{S}}_l$ (i.e., response of the first stage). For each residual unit, its input is added to the output before the ReLU layer. The basic idea is that, rather than expecting blocks to approximate the regression relationship, we explicitly let these layers approximate a residual function, which is easier to be optimized. Finally, fully connected and regression layers are induced to get the predicted score y_i. An average pooling layer is also applied to help prevent overfitting.

Figure 2.

Quantile q and learning rate η (red circle) were optimized by grid search algorithm which minimizes mean absolute error (MAE) of the proposed CNN and fully connected network (FCN) to predict expressive and receptive language scores across 37 children with focal epilepsy.

Figure 3.

Significant linear correlations between the measured and predicted language scores obtained from 37 children with focal epilepsy. To predict the score of each child as a validation trial, we first applied synthetic minority over-sampling technique (SMOTE) to augment the training data, $\left[{\overline{S}}_i,t_i\right]$ of the remaining 36 children. The trained CNN was then used to predict the score of the validation child, y_i as plotted in the y-axis.

Figure 4.

AAL brain regions learned by CNN to be most predictive of the expressive language (top) and receptive language scores (bottom). Each 2D circular connectome presents Circos ideogram available at http://mkweb.bcgsc.ca/tableviewer/. It shows AAL regions and their pair-wise edge strength, ${\overline{S}}_i(m,n)$, quantified by the thickness of individual strips, which are most predictive of the CNN determined score, y_i. Very small magnitudes of positive derivatives (i.e., less than 5 % of maximum value) were omitted for clarity. On the 3D surface images (right panels), the size of each sphere indicates the sum of the positive partial derivative magnitude of edge strength, ${\overline{S}}_i(m,n)$, with respect to the partial derivative of score output, y_i. Here, the greater sphere suggests the region whose edge strengths are more predictive of the score. Note that edge strengths of the right putamen (PUT.R) were found to be the most predictive of expressive language scores, while edge strengths of the left superior parietal gyrus (SPG. L) were the most predictive of receptive language scores. A complete list of region names corresponding to the region labels is available in Table 1.