The corticospinal tract profile in amyotrophic lateral sclerosis

Abstract

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Abnormal cerebral microstructures revealed by diffusion kurtosis imaging in amyotrophic lateral sclerosis

“…Although several WM regions showed decreased AK values (data not shown), they did not reach statistical significance. Several previous DTI studies have provided results consistent with ours, in which no difference in axial diffusivity, a metric indicating the diffusion parallel to the axon, was found in ALS patients . Unfortunately, the specific reasons for the absence of diffusion axial measurement changes are not well understood for ALS at present .…”

Multidimensional analysis and detection of informative features in diffusion MRI measurements of human white matter

“…Figure 4 depicts these coefficients along the right CST, plotted 289 over the FA values for the control and ALS subject groups. We find that SGL selects 290 FA metrics in the corticospinal tract and particularly in the right corticospinal tract as 291 most important to ALS classification, confirming previous findings [51][52][53][54][55][56][57][58][59][60] and 292 identifying the portions of the brain that were selected a priori in the previous study 293 from which we collected our data [3].…”

“…The method is broadly applicable to a wide range of research questions: it 336 performs well in predicting both continuous variables, such as biological age, as well as 337 categorical variables, such as whether a person is a patient or a healthy control. In both 338 of these cases, SGL out-performs previous algorithms that have been developed for 339 these tasks [3,62]. The nested cross-validation approach used to fit the model and make 340 both predictions and inferences from the model guards against overfitting and tunes the 341 degree of sparseness required by the algorithm.…”

“…The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. We demonstrate the accuracy of the method in two settings: i) In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls [3]. Furthermore, SGL automatically identifies FA in the corticospinal tract as important for this classification -correctly finding the parts of the white matter known to be affected by the disease.…”

“…89 Because the features measured with tractomery lend themselves to grouping based on 90 the tracts from which each measurement is taken, GL and SGL could provide a useful 91 tool for linear model fitting in problems of this form. Here we, first, develop an 92 implementation of SGL that is well suited to the analysis of tractometry data and, 93 second, demonstrate the power and flexibility of this approach by applying it to both 94 classification (disease diagnosis) and continuous prediction (age) problems from 95 previously published studies [3,4]. Two different previously-published datasets were used here: .…”