Prediction of Impaired Performance in Trail Making Test in MCI Patients With Small Vessel Disease Using DTI Data

Abstract

Abstract-Mild cognitive impairment (MCI) is a common condition in patients with diffuse hyperintensities of cerebral white matter (WM) in T2-weighted magnetic resonance images and cerebral small vessel disease (SVD). In MCI due to SVD, the most prominent feature of cognitive impairment lies in degradation of executive functions, i.e. of processes which supervise the organization and execution of complex behavior. The Trail Making Test (TMT) is a widely employed test sensitive to cognitive processing speed and executive functioning. MCI due to SVD has been hypothesized to be the effect of WM damage and diffusion tensor imaging (DTI) is a well-established technique for in-vivo characterization of WM. We propose a machine learning scheme tailored to 1) predicting the impairment in executive functions in patients with MCI and SVD, and 2) examining the brain substrates of this impairment. We employed data from 40 MCI patients with SVD and created feature vectors by averaging mean diffusivity (MD) and fractional anisotropy maps within 50 WM regions of interest. We trained support vector machines (SVMs) with polynomial as well as radial basis function kernels using different DTI-derived features while simultaneously optimizing parameters in leave-one-out nested crossvalidation. The best performance was obtained using MD features only and linear kernel SVMs, which were able to distinguish an impaired performance with high sensitivity (72.7%-89.5%), specificity (71.4%-83.3%) and accuracy (77.5%-80.0%). While brain substrates of executive functions are still debated, feature ranking confirm that MD in several WM regions, not limited to the frontal lobes, are truly predictive of executive functions.

Independent value added by diffusion MRI for prediction of cognitive function in older adults

“…We have shown that global MD does have a strong association with executive function as well as which tracts have the strongest associations. In agreement with previous studies of older adults with cerebral small vessel disease ( Jacobs et al, 2013 , Ciulli et al, 2016 ), the results of our comprehensive ROI analyses, by individual tracts and by PCA, suggest that poorer white matter microstructure throughout the cortex, not exclusively frontal associated tracts, may serve as an effective indicator of executive function performance. We found that the majority of projection and associative fibers of the cerebral cortex ( Components 1 and 2 ) were associated with contemporaneous ADNI-EF, indicating minimal regional specificity in the relationship between MD and ADNI-EF.…”

“…Many of these studies selected white matter ROIs a priori , which obviated correlations with other white matter regions. The few studies that surveyed a range of cortical tracts reported associations between higher MD in associative and commissural fibers throughout the cortex and poorer executive function, but did not formally test whether global dMRI associations with executive function were present ( Jacobs et al, 2013 , Ciulli et al, 2016 ). We have shown that global MD does have a strong association with executive function as well as which tracts have the strongest associations.…”

Classification of Subcortical Vascular Cognitive Impairment Using Single MRI Sequence and Deep Learning Convolutional Neural Networks

“…Previous studies made efforts to address this challenging task by traditional machine learning using multimodal MRI data, including diffusion and morphometry features, and eventually proved the usefulness of machine learning techniques in discriminating HCs from patients suffering from VaD and its prodromal stage: vascular mild cognitive impairment (VaMCI) (Diciotti et al, 2015), which represents a transitional state between non-cognitive impairment (NCI) and dementia. Another study implemented SVM-based machine learning strategy for discrimination between SVCI patients with different cognitive performances through predefined feature vectors extracted from diffusion tensor imaging (DTI) data alone (Ciulli et al, 2016). However, the sensitivity (72.7–89.5%), specificity (71.4–83.3%), and accuracy (77.5–80.0%) are not high enough, probably due to the limited generalization ability of the artificial features.…”

White Matter Abnormalities and Correlation With Severity in Normal Tension Glaucoma: A Whole Brain Atlas-Based Diffusion Tensor Study

“…Thus, we attempted to use other DTI methods to investigate the abnormalities of whole brain WM fibers. Atlas-based diffusion tensor analysis (ABA) is a novel method in which each brain is parcellated into 50 anatomic units, 12,13 which can effectively detect the integrity of the whole brain WM. Because it can reduce measurement error 14 and improve statistical power 15 when compared with ROI-based and voxel-based DTI analyses, respectively, the ABA method has recently been applied to investigate normal or abnormal neurodevelopment.…”