Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease in which cerebral structural impairment is a consistent feature. Purpose To investigate cerebral microstructural changes in ALS using diffusion kurtosis imaging (DKI) for the first time. Study Type Prospective. Subjects Eighteen ALS patients and 20 healthy controls. Field Strength/Sequence DKI images were obtained by a spin‐echo echo‐planar imaging sequence on a 3T MRI scanner, with three b‐values (0, 1000, and 2000 s/mm2) and 64 diffusion encoding directions. Assessment The revised ALS Functional Rating Scale (ALSFRS‐R) was administered to assess disease severity, and the symptom duration and disease progression rate were also recorded. Voxel‐based analysis was applied to examine the alteration of DKI metrics (ie, mean kurtosis metrics [MK], axial kurtosis [AK], and radial kurtosis [RK]) and the conventional diffusion metrics (ie, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity). Statistical Tests Student's t‐test, chi‐square test, and Pearson correlation analysis. Results ALS patients showed MK reductions in gray matter areas, including the bilateral precentral gyrus, bilateral paracentral lobule, and left anterior cingulate gyrus; they also showed decreased MK values in white matter (WM) in the bilateral precentral gyrus, bilateral corona radiata, bilateral middle corpus callosum, left occipital lobe, and right superior parietal lobule. The spatial distribution of the regions with reduced RK was similar to those with decreased MK. No significant AK difference was found between groups. The correlation analysis revealed significant associations between DKI metrics and clinical assessments such as ALSFRS‐R score and disease duration. Additionally, several WM regions showed between‐group differences in conventional diffusion metrics; but the spatial extent was smaller than that with reduced DKI metrics. Data Conclusion The reduction in DKI metrics indicates decreased microstructural complexity in ALS, involving both motor‐related areas and extramotor regions. DKI metrics can serve as potential biomarkers for assessing disease severity. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:554–562.

Objective. Fractal dimensionality (FD) analysis provides a quantitative description of brain structural complexity. The application of FD analysis has provided evidence of amyotrophic lateral sclerosis- (ALS-) related white matter degeneration. This study is aimed at evaluating, for the first time, FD alterations in a gray matter in ALS and determining its association with clinical parameters. Materials and Methods. This study included 22 patients diagnosed with ALS and 20 healthy subjects who underwent high-resolution T1-weighted imaging scanning. Disease severity was assessed using the revised ALS Functional Rating Scale (ALSFRS-R). The duration of symptoms and rate of disease progression were also assessed. The regional FD value was calculated by a computational anatomy toolbox and compared among groups. The relationship between cortical FD values and clinical parameters was evaluated by Spearman correlation analysis. Results. ALS patients showed decreased FD values in the left precentral gyrus and central sulcus, left circular sulcus of insula (superior segment), left cingulate gyrus and sulcus (middle-posterior part), right precentral gyrus, and right postcentral gyrus. The FD values in the right precentral gyrus were positively correlated to ALSFRS-R scores (r=0.44 and P=0.023), whereas negatively correlated to the rate of disease progression (r=−0.41 and P=0.039). Meanwhile, the FD value in the left circular sulcus of the insula (superior segment) was negatively correlated to disease duration (r=−0.51 and P=0.010). Conclusions. Our results suggest an ALS-related reduction in structural complexity involving the gray matter. FD analysis may shed more light on the pathophysiology of ALS.

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Background: Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS-related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders. Purpose: To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity. Study Type: Prospective. Subjects: Thirty-two ALS patients and 45 healthy controls. Field Strength/Sequence: Multiband resting-state functional images using gradient echo echo-planar imaging and T1weighted images were acquired at 3.0 T. Assessment: Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS-R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive-control (CCN)/default-mode (DMN)/subcortical/cerebellar networks). A sliding-window correlation approach was used to compute dFNC. FNC states were determined by k-mean clustering, and state-specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated. Statistical Tests: Two-sample t test used for comparisons on dynamic measures and Spearman's correlation analysis. Results: ALS patients showed increased FNC between DMN-SMN in state 1 and between CCN-SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 AE 40.1 vs. 93.6 AE 126.3; P < 0.05) and remained in state 1 (showing a relatively strong FNC) for a shorter time (fraction time: 0.27 AE 0.25 vs. 0.13 AE 0.20; P < 0.05). ALS patients exhibited less temporal variability in their FNC (transition number: 10.2 AE 4.4 vs. 7.8 AE 3.8; P < 0.05). A significant correlation was observed between ALSFRS-R and mean dwell time in state 2 (r = −0.414, P < 0.05) and transition number (r = 0.452, P < 0.05). No significant between-subgroup difference in dFNC properties was found (all P > 0.05). Data Conclusion: Our findings suggest aberrant dFNC properties in ALS, which is associated with disease severity. Level of Evidence: 2 Technical Efficacy: Stage 3

Objectives: White matter (WM) impairments involving both motor and extra-motor areas have been well-documented in amyotrophic lateral sclerosis (ALS). This study tested the potential of diffusion measurements in WM for identifying ALS based on support vector machine (SVM). Methods: Voxel-wise fractional anisotropy (FA) values of diffusion tensor images (DTI) were extracted from 22 ALS patients and 26 healthy controls and served as discrimination features. The revised ALS Functional Rating Scale (ALSFRS-R) was employed to assess ALS severity. Feature ranking and selection were based on Fisher scores. A linear kernel SVM algorithm was applied to build the classification model, from which the classification performance was evaluated. To promote classifier generalization ability, a leave-one-out cross-validation (LOOCV) method was adopted. Results: By using the 2,400∼3,400 ranked features as optimal features, the highest classification accuracy of 83.33% (sensitivity = 77.27% and specificity = 88.46%, P = 0.0001) was achieved, with an area under receiver operating characteristic curve of 0.862. The predicted function value was positively correlated with patient ALSFRS-R scores (r = 0.493, P = 0.020). In the optimized SVM model, FA values from several regions mostly contributed to classification, primarily involving the corticospinal tract pathway, postcentral gyrus, and frontal and parietal areas. Conclusions: Our results suggest the feasibility of ALS diagnosis based on SVM analysis and diffusion measurements of WM. Additional investigations using a larger cohort is recommended in order to validate the results of this study.