Brainstem Involvement in Amyotrophic Lateral Sclerosis: A Combined Structural and Diffusion Tensor MRI Analysis

Abstract

IntroductionThe brainstem is an important component in the pathology of amyotrophic lateral sclerosis (ALS). Although neuroimaging studies have shown multiple structural changes in ALS patients, few studies have investigated structural alterations in the brainstem. Herein, we compared the brainstem structure between patients with ALS and healthy controls.MethodsA total of 33 patients with ALS and 33 healthy controls were recruited in this study. T1-weighted and diffusion tensor imaging (DTI) were acquired on a 3 Tesla magnetic resonance imaging (3T MRI) scanner. Volumetric and vertex-wised approaches were implemented to assess the differences in the brainstem’s morphological features between the two groups. An atlas-based region of interest (ROI) analysis was performed to compare the white matter integrity of the brainstem between the two groups. Additionally, a correlation analysis was used to evaluate the relationship between ALS clinical characteristics and structural features.ResultsVolumetric analyses showed no significant difference in the subregion volume of the brainstem between ALS patients and healthy controls. In the shape analyses, ALS patients had a local abnormal surface contraction in the ventral medulla oblongata and ventral pons. Compared with healthy controls, ALS patients showed significantly lower fractional anisotropy (FA) in the left corticospinal tract (CST) and bilateral frontopontine tracts (FPT) at the brainstem level, and higher radial diffusivity (RD) in bilateral CST and left FPT at the brainstem level by ROI analysis in DTI. Correlation analysis showed that disease severity was positively associated with FA in left CST and left FPT.ConclusionThese findings suggest that the brainstem in ALS suffers atrophy, and degenerative processes in the brainstem may reflect disease severity in ALS. These findings may be helpful for further understanding of potential neural mechanisms in ALS.

Morphological similarity of amygdala-ventral prefrontal pathways represents trait anxiety in younger and older adults

“…Prior to further processing images, quality checks on diffusion images were performed using QUAD and SQUAD, the automated quality control tool in FSL ( 79 ). Results indicated that all participants fell under the exclusion threshold from previous literature, which was average volume-to-volume head motion of <3 mm or <5% of total outliers ( 80 , 81 ). Visual inspection on the cleaned images confirmed this result, but because diffusion images are especially susceptible to head motion ( 82 ), head motion effects were further tested, as discussed in Control Analyses .…”

Clinical Profiles and Patterns of Neurodegeneration in Amyotrophic Lateral Sclerosis: A Cluster-Based Approach Based on MR Imaging Metrics

“…Unlike in interesting previous studies that first applied this approach, 26,27 we focused our cluster-based analysis on CNS-selected areas that are already found to be associated with survival 20,22 and disease severity. 17,21,40 Restricting the cluster-based analysis to the motor cortical areas and medulla oblongata, we first found that a considerable proportion (57%) of patients with ALS (belonging to cluster 2) showed greater involvement of the medulla already at the time of diagnosis. Second, a small subgroup of the ALS cohort (cluster 3, 12%) had early involvement of the motor and extramotor cortices with relative preservation of the medulla oblongata.…”

“…13 Specifically, disease severity (expressed as ALS Functional Rating Scale-Revised [ALSFRS-r] score) and progression rate correlated with the mean cortical thickness of the motor area, [14][15][16][17][18] extramotor areas (eg, paracentral lobules), 17,19 and medulla oblongata volume (MOv). [19][20][21] These MR imaging metrics have also been proposed as predictive biomarkers of survival. 20,22 Nonetheless, most imaging studies validated MR imaging metrics by describing phenotype-, genotype-, or stage-associated radiologic profiles [23][24][25] in a priori selected clinical categorizations (eg, spinal or bulbar onset, fast and slow progressors, prevalent upper or lower motor neuron impairment).…”

Mathematical and Statistical Review of NMR Time Domain Data Pre-Processing

“…Nuclear Magnetic Resonance (NMR) spectroscopy is a highly effective analytical tool for providing intricate details about the molecular structure, composition, and dynamics of a sample. It has also given rise to many new techniques, including Magnetic Resonance Spectrometry (MRS), Nuclear Magnetic Resonance Imaging (MRI), Functional MRI (fMRI), Diffusion MRI (dMRI), and Diffusion Tensor Imaging (DTI) [1][2][3][4][5] . These NMR techniques find widespread applications in fields such as chemistry, biology, agriculture, and medicine [6][7][8][9] .…”