Corticospinal tract degeneration in ALS unmasked in T1‐weighted images using texture analysis

Abstract

The purpose of this study was to investigate whether textures computed from T1‐weighted (T1W) images of the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS) are associated with degenerative changes evaluated by diffusion tensor imaging (DTI). Nineteen patients with ALS and 14 controls were prospectively recruited and underwent T1W and diffusion‐weighted magnetic resonance imaging. Three‐dimensional texture maps were computed from T1W images and correlated with the DTI metrics within the CST. Significantly correlated textures were selected and compared within the CST for group differences between patients and controls using voxel‐wise analysis. Textures were correlated with the patients' clinical upper motor neuron (UMN) signs and their diagnostic accuracy was evaluated. Voxel‐wise analysis of textures and their diagnostic performance were then assessed in an independent cohort with 26 patients and 13 controls. Results showed that textures autocorrelation, energy, and inverse difference normalized significantly correlated with DTI metrics (p < .05) and these textures were selected for further analyses. The textures demonstrated significant voxel‐wise differences between patients and controls in the centrum semiovale and the posterior limb of the internal capsule bilaterally (p < .05). Autocorrelation and energy significantly correlated with UMN burden in patients (p < .05) and classified patients and controls with 97% accuracy (100% sensitivity, 92.9% specificity). In the independent cohort, the selected textures demonstrated similar regional differences between patients and controls and classified participants with 94.9% accuracy. These results provide evidence that T1‐based textures are associated with degenerative changes in the CST.

Reliability of 3D texture analysis: A multicenter MRI study of the brain

“…The SMART (Statistical MAp fRom Texture) toolbox was used to perform 3D TA . The voxel‐based GLCM in 3D (VGLCM‐TOP‐3D) method was executed on the bias‐corrected images in native space.…”

“…However, voxelwise 3D TA instead computes the statistical differences in texture values. This technique has shown to be highly sensitive and specific in both Alzheimer's disease and ALS, supporting its potential as an imaging biomarker and diagnostic tool …”

“…TA has potential for clinical use, providing information on many neurological disorders. Indeed, it has been used to study intracranial brain tumors, epilepsy, multiple sclerosis, Alzheimer's disease, and amyotrophic lateral sclerosis (ALS) …”

“…Indeed, it has been used to study intracranial brain tumors, 3,4 epilepsy, 5,6 multiple sclerosis, 1,7,8 Alzheimer's disease, [9][10][11][12][13] and amyotrophic lateral sclerosis (ALS). [14][15][16][17] Previous methods of using TA have been limited to 2D region-of-interest (ROI)-based approaches. To address this limitation, a novel 3D TA technique based on the popular gray-level co-occurrence matrix (GLCM) method was previously developed.…”

“…This technique has shown to be highly sensitive and specific in both Alzheimer's disease and ALS, supporting its potential as an imaging biomarker and diagnostic tool. [12][13][14][15][16][17] The study of neurological disorders has been facilitated by growing multicenter collaborative efforts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). 18 Such consortia have been successful in increasing the pool of MRI data, and thus statistical power allowing for increased sensitivity to subtle changes in neuroimaging metrics.…”