Magnetic resonance metrics to evaluate the effect of therapy in amyotrophic lateral sclerosis: the experience with edaravone

Update on recent advances in amyotrophic lateral sclerosis

In the last few years, our understanding of disease molecular mechanisms underpinning ALS has advanced greatly, allowing the first steps in translating into clinical practice novel research findings, including gene therapy approaches. Similarly, the recent advent of assistive technologies has greatly improved the possibility of a more personalized approach to supportive and symptomatic care, in the context of an increasingly complex multidisciplinary line of actions, which remains the cornerstone of ALS management. Against this rapidly growing background, here we provide an comprehensive update on the most recent studies that have contributed towards our understanding of ALS pathogenesis, the latest results from clinical trials as well as the future directions for improving the clinical management of ALS patients.

From use of omics to systems biology: Identifying therapeutic targets for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a heterogeneous progressive neurodegenerative disorder with available treatments such as riluzole and edaravone extending survival by an average of 3-6 months. The lack of highly effective, widely available therapies reflects the complexity of ALS. Omics technologies, including genomics, transcriptomic and proteomics have contributed to the identification of biological pathways dysregulated and targeted by therapeutic strategies in preclinical and clinical trials. Integrating clinical, environmental and neuroimaging information with omics data and applying a systems biology approach can further improve our understanding of the disease with the potential to stratify patients and provide more personalised medicine. This chapter will review the omics technologies that contribute to a systems biology approach and how these components have assisted in identifying therapeutic targets. Current strategies, including the use of genetic screening and biosampling in clinical trials, as well as the future application of additional technological advances, will also be discussed.

The impact of upper and lower motor neuron burden on diagnostic certainty, and clinical course of spinal-onset amyotrophic lateral sclerosis: a cluster-based approach

BACKGROUND

Upper motor neuron (UMN) and lower motor neuron (LMN) involvement represent the core clinical features of amyotrophic lateral sclerosis (ALS). Several studies divided patients into prevalent UMN and LMN impairment phenotypes to investigate the association between motor systems impairments and ALS clinical course. However, this distinction was somehow heterogeneous and significantly affected the comparability across studies.

AIMS

This study aimed to investigate whether patients spontaneously segregate based on the extent of UMN and LMN involvement without a-priori categorization and to identify potential clinical and prognostic features of different clusters.

METHODS

Eighty-eight consecutive spinal-onset ALS patients were referred to an ALS tertiary center between 2015 and 2022. UMN and LMN burden was assessed with the Penn Upper Motor Neuron scale (PUMNS) and the Devine score, respectively. PUMNS and LMN scores were normalized into 0-1 and analyzed using a two-step cluster analysis and the Euclidean distance measure. The Bayesian Information Criterion was used to determine the cluster number. Demographic and clinical variables were tested for differences among the clusters.

RESULTS

Three distinct clusters emerged at cluster analysis. Patients in "cluster-1" showed moderate UMN and severe LMN involvement, corresponding to the typical ALS phenotype. Patients in "cluster-2" showed mild LMN and severe UMN damage, corresponding to a predominant UMN phenotype, while "cluster-3" patients showed mild UMN and moderate LMN damage, corresponding to a predominant LMN phenotype. Patients in "cluster-1" and "cluster-2" showed a higher prevalence of definite ALS than those in "cluster-3" (61% and 46 vs 9%, p < 0.001). "Cluster-1" patients had a lower median ALSFRS-r score compared to both "cluster-2" and 3 patients (27 vs 40 and 35, < 0.001). "Cluster-1" (HR: 8.5; 95% CI 2.1-35.1 and p = 0.003) and 3 (HR: 3.2; 95% CI 1.1-9.1; p = 0.03) were associated with shorter survival than those in "cluster-2".

CONCLUSIONS

Spinal-onset ALS can be categorized into three groups according to LMN and UMN burden. The UMN burden is related to higher diagnostic certainty and broader disease spread, while LMN involvement is associated with higher disease severity and shorter survival.

Acoustic Voice Analysis as a Useful Tool to Discriminate Different ALS Phenotypes

Approximately 80-96% of people with amyotrophic lateral sclerosis (ALS) become unable to speak during the disease progression. Assessing upper and lower motor neuron impairment in bulbar regions of ALS patients remains challenging, particularly in distinguishing spastic and flaccid dysarthria. This study aimed to evaluate acoustic voice parameters as useful biomarkers to discriminate ALS clinical phenotypes. Triangular vowel space area (tVSA), alternating motion rates (AMRs), and sequential motion rates (SMRs) were analyzed in 36 ALS patients and 20 sex/age-matched healthy controls (HCs). tVSA, AMR, and SMR values significantly differed between ALS and HCs, and between ALS with prevalent upper (pUMN) and lower motor neuron (pLMN) impairment. tVSA showed higher accuracy in discriminating pUMN from pLMN patients. AMR and SMR were significantly lower in patients with bulbar onset than those with spinal onset, both with and without bulbar symptoms. Furthermore, these values were also lower in patients with spinal onset associated with bulbar symptoms than in those with spinal onset alone. Additionally, AMR and SMR values correlated with the degree of dysphagia. Acoustic voice analysis may be considered a useful prognostic tool to differentiate spastic and flaccid dysarthria and to assess the degree of bulbar involvement in ALS.

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

BACKGROUND AND PURPOSE

The previous studies described phenotype-associated imaging findings in amyotrophic lateral sclerosis (ALS) with a prior categorization of patients based on clinical characteristics. We investigated the natural segregation of patients through a radiologic cluster-based approach without a priori patient categorization using 3 well-known prognostic MR imaging biomarkers in ALS, namely bilateral precentral and paracentral gyrus cortical thickness and medulla oblongata volume. We aimed to identify clinical/prognostic features that are cluster-associated.

MATERIALS AND METHODS

Bilateral precentral and paracentral gyri and medulla oblongata volume were calculated using FreeSurfer in 90 patients with amyotrophic lateral sclerosis and 25 healthy controls. A 2-step cluster analysis was performed using precentral and paracentral gyri (averaged pair-wise) and medulla oblongata volume.

RESULTS

We identified 3 radiologic clusters: 28 (31%) patients belonged to "cluster-1"; 51 (57%), to "cluster 2"; and 11 (12%), to "cluster 3." Patients in cluster 1 showed statistically significant cortical thinning of the analyzed cortical areas and lower medulla oblongata volume compared with subjects in cluster 2 and cluster 3, respectively. Patients in cluster 3 exhibited significant cortical thinning of both paracentral and precentral gyri versus those in cluster 2, and this latter cluster showed lower medulla oblongata volume than cluster 3. Patients in cluster 1 were characterized by older age, higher female prevalence, greater disease severity, higher progression rate, and lower survival compared with patients in clusters 2 and 3.

CONCLUSIONS

Patients with amyotrophic lateral sclerosis spontaneously segregate according to age and sex-specific patterns of neurodegeneration. Some patients with amyotrophic lateral sclerosis showed an early higher impairment of cortical motor neurons with relative sparing of bulbar motor neurons (cluster 3), while others expressed an opposite pattern (cluster 2). Moreover, 31% of patients showed an early simultaneous impairment of cortical and bulbar motor neurons (cluster 1), and they were characterized by higher disease severity and lower survival.

Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development

Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.

Medulla oblongata volume as a promising predictor of survival in amyotrophic lateral sclerosis

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Brainstem volumes reflect the disease severity expressed as ALSFRS-r (total score and its bulbar and spinal subscores).

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Medulla oblongata volume demonstrated a significant accuracy to discriminate long and short survivors ALS patients.

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Brainstem volumes may reflect the impairment of corticospinal and corticobulbar tracts as well as lower bulbar motor neurons. Furthermore, medulla oblongata could be used as an early predictor of survival in ALS patients.

Background

Unconventional magnetic resonance imaging studies of the brainstem have recently acquired a growing interest in amyotrophic lateral sclerosis (ALS) pathology since they provide a unique opportunity to evaluate motor tract degeneration and bulbar lower motor neuron involvement. The aim of this study was to investigate the role of brainstem structures as accurate biomarkers of disease severity and predictors of survival.

Materials and Methods

A total of 60 ALS patients and 30 healthy controls subjects (CS) were recruited in this study. Patients were divided in two subgroups according to the onset of the disease: 42 spinal (S-ALS) and 18 bulbar (B-ALS). All subjects underwent 3D-structural MRI. Brainstem volume both of the entire cohort of ALS patients and S-ALS and B-ALS onset were compared with those of CS. In addition the two ALS subgroups were tested for differences in brainstem volumes.

Volumetric, vertex-wise, and voxel-based approaches were implemented to assess correlations between MR structural features and clinical characteristics expressed as ALSFRS-r and its bulbar (ALSFSR-r-B) and spinal subscores (ALSFSR-r-S). ROC curves were performed to test the accuracy of midbrain, pons, and medulla oblongata volumes able to discriminate patients dichotomized into long and short survivors by using Two-Steps cluster analysis. Univariate and multivariate survival analyses were carried out to test the prognostic role of brainstem structures’ volume, trichotomized by applying a k-means clustering algorithm.

Results

Both the entire cohort of ALS patients and B-ALS and S-ALS showed significant lower volumes of both medulla oblongata and pons compared to CS. Furthermore, B-ALS showed a significant lower volume of medulla oblongata, compared to S-ALS. Lower score of ALSFRS-r correlated to atrophy in the anterior compartment of midbrain, pons, and medulla oblongata, as well as in the posterior portion of only this latter region. ALSFSR-r-S positively correlated with shape deformation and density reduction of the anterior portion of the entire brainstem, along the corticospinal tracts. ALSFSR-r-B instead showed a positive correlation with shape deformation of the floor of the fourth ventricle in the medulla oblongata and the crus cerebri in the midbrain. Only medulla oblongata volume demonstrated a significant accuracy to discriminate long and short survivors ALS patients (ROC AUC 0.76, p < 0.001). Univariate and multivariate analysis confirmed the survival predictive role of the medulla oblongata (log rank test p: 0.003).

Discussions

Our findings suggest that brainstem volume may reflect the impairment of corticospinal and corticobulbar tracts as well as lower bulbar motor neurons. Furthermore, medulla oblongata could be used as an early predictor of survival in ALS patients.