Advanced neuroimaging approaches in amyotrophic lateral sclerosis: refining the clinical diagnosis

Brain MRI abnormalities associated with amyotrophic lateral sclerosis: A case illustration

Amyotrophic lateral sclerosis is a progressive neurodegenerative pathology. It involves both upper and lower motor neurons, leading to their degeneration. Lower motor neurons can be detected with an electromyogram, but the detection of upper motor neuron dysfunction may be more accurate using MRI. We present the case of a 64-year-old woman with amyotrophic lateral sclerosis, presenting the motor band sign and the bright tongue sign on MRI.

Current State and Future Directions in the Diagnosis of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of upper and lower motor neurons, resulting in progressive weakness of all voluntary muscles and eventual respiratory failure. Non-motor symptoms, such as cognitive and behavioral changes, frequently occur over the course of the disease. Considering its poor prognosis with a median survival time of 2 to 4 years and limited causal treatment options, an early diagnosis of ALS plays an essential role. In the past, diagnosis has primarily been determined by clinical findings supported by electrophysiological and laboratory measurements. To increase diagnostic accuracy, reduce diagnostic delay, optimize stratification in clinical trials and provide quantitative monitoring of disease progression and treatment responsivity, research on disease-specific and feasible fluid biomarkers, such as neurofilaments, has been intensely pursued. Advances in imaging techniques have additionally yielded diagnostic benefits. Growing perception and greater availability of genetic testing facilitate early identification of pathogenic ALS-related gene mutations, predictive testing and access to novel therapeutic agents in clinical trials addressing disease-modified therapies before the advent of the first clinical symptoms. Lately, personalized survival prediction models have been proposed to offer a more detailed disclosure of the prognosis for the patient. In this review, the established procedures and future directions in the diagnostics of ALS are summarized to serve as a practical guideline and to improve the diagnostic pathway of this burdensome disease.

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.

Longitudinal monitoring of amyotrophic lateral sclerosis by diffusion tensor imaging: Power calculations for group studies

Introduction

Diffusion tensor imaging (DTI) can be used to map disease progression in amyotrophic lateral sclerosis (ALS) and therefore is a promising candidate for a biomarker in ALS. To this end, longitudinal study protocols need to be optimized and validated regarding group sizes and time intervals between visits. The objective of this study was to assess the influences of sample size, the schedule of follow-up measurements, and measurement uncertainties on the statistical power to optimize longitudinal DTI study protocols in ALS.

Patients and methods

To estimate the measurement uncertainty of a tract-of–interest-based DTI approach, longitudinal test-retest measurements were applied first to a normal data set. Then, DTI data sets of 80 patients with ALS and 50 healthy participants were analyzed in the simulation of longitudinal trajectories, that is, longitudinal fractional anisotropy (FA) values for follow-up sessions were simulated for synthetic patient and control groups with different rates of FA decrease in the corticospinal tract. Monte Carlo simulations of synthetic longitudinal study groups were used to estimate the statistical power and thus the potentially needed sample sizes for a various number of scans at one visit, different time intervals between baseline and follow-up measurements, and measurement uncertainties.

Results

From the simulation for different longitudinal FA decrease rates, it was found that two scans per session increased the statistical power in the investigated settings unless sample sizes were sufficiently large and time intervals were appropriately long. The positive effect of a second scan per session on the statistical power was particularly pronounced for FA values with high measurement uncertainty, for which the third scan per session increased the statistical power even further.

Conclusion

With more than one scan per session, the statistical power of longitudinal DTI studies can be increased in patients with ALS. Consequently, sufficient statistical power can be achieved even with limited sample sizes. An improved longitudinal DTI study protocol contributes to the detection of small changes in diffusion metrics and thereby supports DTI as an applicable and reliable non-invasive biomarker in ALS.

Simultaneous PET/MRI: The future gold standard for characterizing motor neuron disease-A clinico-radiological and neuroscientific perspective

Neuroimaging assessment of motor neuron disease has turned into a cornerstone of its clinical workup. Amyotrophic lateral sclerosis (ALS), as a paradigmatic motor neuron disease, has been extensively studied by advanced neuroimaging methods, including molecular imaging by MRI and PET, furthering finer and more specific details of the cascade of ALS neurodegeneration and symptoms, facilitated by multicentric studies implementing novel methodologies. With an increase in multimodal neuroimaging data on ALS and an exponential improvement in neuroimaging technology, the need for harmonization of protocols and integration of their respective findings into a consistent model becomes mandatory. Integration of multimodal data into a model of a continuing cascade of functional loss also calls for the best attempt to correlate the different molecular imaging measurements as performed at the shortest inter-modality time intervals possible. As outlined in this perspective article, simultaneous PET/MRI, nowadays available at many neuroimaging research sites, offers the perspective of a one-stop shop for reproducible imaging biomarkers on neuronal damage and has the potential to become the new gold standard for characterizing motor neuron disease from the clinico-radiological and neuroscientific perspectives.

Trends in global amyotrophic lateral sclerosis research from 2000 to 2022: A bibliometric analysis

Background

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disease affecting the motor neurons. Although much research has been conducted in this field, few bibliometric studies have been conducted. This study aimed to provide an overview of publishing characteristics and trends in ALS research since 2000 using a bibliometric analysis.

Methods

We conducted a comprehensive literature search in the Web of Science (WOS) Core Collection database for scientific output related to ALS from 2000 to 2022. The retrieved dataset was refined using Google OpenRefine and analyzed using bibliometrix.

Results

A total of 29,391 articles published since 2000 were retrieved, with an average annual growth rate of 6.35%. Ninety-six countries and regions contributed to ALS research, among which the United States had the dominant position with the highest number of publications (n = 8,202) and citations (n = 558,561). An association analysis was performed to form networks of country collaboration and keyword co-occurrence. The evolution of topic trends was demonstrated in terms of both frequency and proportion.

Conclusion

The output of ALS research has increased steadily over the years, and the United States and Western Europe are leaders in this field. There is an upgradation in the pathomechanism and clinical research on ALS.

Relaxation-weighted 23Na magnetic resonance imaging maps regional patterns of abnormal sodium concentrations in amyotrophic lateral sclerosis

Objectives:

Multiparametric magnetic resonance imaging (MRI) is established as a technical instrument for the characterisation of patients with amyotrophic lateral sclerosis (ALS). The contribution of relaxation-weighted sodium (²³NaR) MRI remains to be defined. The aim of this study is to apply ²³NaR MRI to investigate brain sodium homeostasis and map potential alterations in patients with ALS as compared with healthy controls.

Materials and Methods:

Seventeen patients with ALS (mean age 61.1 ± 11.4 years, m/f = 9/8) and 10 healthy control subjects (mean age 60.3 ± 15.3 years, m/f = 6/4) were examined by ²³NaR MRI at 3 T. Regional sodium maps were obtained by the calculation of the weighted difference from two image data sets with different echo times (TE₁ = 0.3 ms, TE₂ = 25 ms). Voxel-based analysis of the relaxation-weighted maps, together with ²³Na concentration maps for comparison, was performed.

Results:

ROI-based analyses of relaxation-weighted brain sodium concentration maps demonstrated increased sodium concentrations in the upper corticospinal tracts and in the frontal lobes in patients with ALS; no differences between ALS patients and controls were found in reference ROIs, where no involvement in ALS-associated neurodegeneration could be anticipated.

Conclusion:

²³NaR MRI mapped regional alterations within disease-relevant areas in ALS which correspond to the stages of the central nervous system (CNS) pathology, providing evidence that the technique is a potential biological marker of the cerebral neurodegenerative process in ALS.

Temporal muscle thickness and survival in patients with amyotrophic lateral sclerosis

Temporal muscle thickness (TMT) is a new potential MRI biomarker, which has shown prognostic relevance in neuro-oncology. We aim at investigating the potential prognostic value of TMT in patients with Amyotrophic Lateral Sclerosis (ALS). We retrospectively evaluated 30 ALS patients, whose clinical, Magnetic Resonance Imaging (MRI) and Electrodiagnostic testing (EDX) data were available, in comparison to age-matched 30 healthy subjects. TMT calculated on T1-weighted MR images was significantly lower in ALS patients than in healthy subjects (p < 0.001), correlating with the ALS Functional Rating Scale (FRS) (p:0.018) and compound motor action potential (CMAP) (p:0.012) in the patients group. Multivariate analysis of overall survival (OS) showed that the only parameters that remained significant were TMT (p:0.002, OR 0.45, 95%vCI: 0.28-0.75) and ALS FRS-R (p:0.023, OR: 0.80, 95%CI: 0.67-0.92). TMT seems to be a promising surrogate biomarker of survival and functional status in ALS. Our data deserve further investigations in multicenter and prospective trials.

Alterations of Microstructure and Sodium Homeostasis in Fast Amyotrophic Lateral Sclerosis Progressors: A Brain DTI and Sodium MRI Study

BACKGROUND AND PURPOSE

While conventional MR imaging has limited value in amyotrophic lateral sclerosis, nonconventional MR imaging has shown alterations of microstructure using diffusion MR imaging and recently sodium homeostasis with sodium MR imaging. We aimed to investigate the topography of brain regions showing combined microstructural and sodium homeostasis alterations in amyotrophic lateral sclerosis subgroups according to their disease-progression rates.

MATERIALS AND METHODS

Twenty-nine patients with amyotrophic lateral sclerosis and 24 age-matched healthy controls were recruited. Clinical assessments included disease duration and the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale. Patients were clinically differentiated into fast (n = 13) and slow (n = 16) progressors according to the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale progression rate. 3T MR imaging brain protocol included ¹H T1-weighted and diffusion sequences and a ²³Na density-adapted radial sequence. Quantitative maps of diffusion with fractional anisotropy, mean diffusivity, and total sodium concentration were measured. The topography of diffusion and sodium abnormalities was assessed by voxelwise analyses.

RESULTS

Patients with amyotrophic lateral sclerosis showed significantly higher sodium concentrations and lower fractional anisotropy, along with higher sodium concentrations and higher mean diffusivity compared with healthy controls, primarily within the corticospinal tracts, corona radiata, and body and genu of the corpus callosum. Fast progressors showed wider-spread abnormalities mainly in the frontal areas. In slow progressors, only fractional anisotropy measures showed abnormalities compared with healthy controls, localized in focal regions of the corticospinal tracts, the body of corpus callosum, corona radiata, and thalamic radiation.

CONCLUSIONS

The present study evidenced widespread combined microstructural and sodium homeostasis brain alterations in fast amyotrophic lateral sclerosis progressors.

Segmental alterations of the corpus callosum in motor neuron disease: A DTI and texture analysis in 575 patients

INTRODUCTION

Within the core neuroimaging signature of amyotrophic lateral sclerosis (ALS), the corpus callosum (CC) is increasingly recognized as a consistent feature. The aim of this study was to investigate the sensitivity and specificity of the microstructural segmental CC morphology, assessed by diffusion tensor imaging (DTI) and high-resolution T1-weighted (T1w) imaging, in a large cohort of ALS patients including different clinical phenotypes.

METHODS

In a single-centre study, 575 patients with ALS (classical phenotype, N  = 432; restricted phenotypes primary lateral sclerosis (PLS) N = 55, flail arm syndrome (FAS) N = 45, progressive bulbar palsy (PBP) N = 22, lower motor neuron disease (LMND) N = 21) and 112 healthy controls underwent multiparametric MRI, i.e. volume-rendering T1w scans and DTI. Tract-based fractional anisotropy statistics (TFAS) was applied to callosal tracts of CC areas I-V, identified from DTI data (tract-of-interest (TOI) analysis), and texture analysis was applied to T1w data. In order to further specify the callosal alterations, a support vector machine (SVM) algorithm was used to discriminate between motor neuron disease patients and controls.

RESULTS

The analysis of white matter integrity revealed predominantly FA reductions for tracts of the callosal areas I, II, and III (with highest reductions in callosal area III) for all ALS patients and separately for each phenotype when compared to controls; texture analysis demonstrated significant alterations of the parameters entropy and homogeneity for ALS patients and all phenotypes for the CC areas I, II, and III (with again highest reductions in callosal area III) compared to controls. With SVM applied on multiparametric callosal parameters of area III, a separation of all ALS patients including phenotypes from controls with 72% sensitivity and 73% specificity was achieved. These results for callosal area III parameters could be improved by an SVM of six multiparametric callosal parameters of areas I, II, and III, achieving a separation of all ALS patients including phenotypes from controls with 84% sensitivity and 85% specificity.

DISCUSSION

The multiparametric MRI texture and DTI analysis demonstrated substantial alterations of the frontal and central CC with most significant alterations in callosal area III (motor segment) in ALS and separately in all investigated phenotypes (PLS, FAS, PBP, LMND) in comparison to controls, while no significant differences were observed between ALS and its phenotypes. The combination of the texture and the DTI parameters in an unbiased SVM-based approach might contribute as a neuroimaging marker for the assessment of the CC in ALS, including subtypes.

Early and progressive dysfunction revealed by in vivo neurite imaging in the rNLS8 TDP-43 mouse model of ALS

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Are neurite density and dispersion altered in amyotropic lateral sclerosis (ALS)?

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Both measures are affected in the rNLS8 TDP-43 mouse model of ALS.

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Diffusion tensor imaging metrics were also affected.

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Group-wise changes were observed early in the disease course.

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Together these diffusion imaging metrics may aid in the timelier diagnosis of ALS.

Amyotrophic lateral sclerosis (ALS) is characterized by transactive response DNA-binding protein 43 (TDP-43) pathology, progressive loss of motor neurons and muscle dysfunction. Symptom onset can be insidious and diagnosis challenging. Conventional neuroimaging is used to exclude ALS mimics, however more advanced neuroimaging techniques may facilitate an earlier diagnosis. Here, we investigate the potential for neurite orientation dispersion and density imaging and diffusion tensor imaging (DTI) to detect microstructural changes in an experimental model of ALS with neuronal doxycycline (Dox)-suppressible overexpression of human TDP-43 (hTDP-43). In vivo diffusion-weighted imaging (DWI) was acquired 1- and 3- weeks following the initiation of hTDP-43 expression (post-Dox) to investigate whether neurite density imaging (NDI) and orientation dispersion imaging (ODI) are affected early in this preclinical model of ALS and if so, how these metrics compare to those derived from the diffusion tensor. Tract-based spatial statistics at 1-week post-Dox, i.e. very early in the disease stage, demonstrated increased NDI in TDP-43 mice but no change in ODI or DTI metrics. At 3-weeks post-Dox, a reduced pattern of increased NDI was observed along with widespread increases in ODI, and decreased fractional anisotropy (FA), apparent diffusion coefficient (ADC) and axial diffusivity (AD). A hypothesis driven analysis of the bilateral corticospinal tracts demonstrated that at 1-week post-Dox, ODI was significantly increased caudally but decreased in the motor cortex of TDP-43 mice. Decreased cortical ODI had normalized by 3-weeks post-Dox and only significant increases were observed. A similar, but inverse pattern in FA was also observed. Together, these results suggest a non-monotonic relationship between DWI metrics and pathophysiological progression with TDP-43 mice exhibiting significantly altered diffusion metrics consistent with early inflammation followed by progressive axonal degeneration. Importantly, significant group-wise changes were observed in the earliest stages of disease when subtle pathology may be more elusive to traditional structural imaging techniques.

Multiparametric Microstructural MRI and Machine Learning Classification Yields High Diagnostic Accuracy in Amyotrophic Lateral Sclerosis: Proof of Concept

The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches. We set out to combine these neuroimaging markers as age-corrected features in a machine learning model with a cohort of 502 subjects, divided into 404 patients with ALS and 98 healthy controls. We calculated a linear support vector classifier (SVC) which is a very robust model and then verified the results with a multilayer perceptron (MLP)/neural network. Both classifiers were able to separate ALS patients from controls with receiver operating characteristic (ROC) curves showing an area under the curve (AUC) of 0.87-0.88 ("good") for the SVC and 0.88-0.91 ("good" to "excellent") for the MLP. Among the coefficients of the SVC, texture data contributed the most to a correct classification. We consider these results as a proof of concept that demonstrated the power of machine learning in the application of multiparametric quantitative neuroimaging data to ALS.

Feature selection from magnetic resonance imaging data in ALS: a systematic review

Background:

With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Methods:

We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported.

Results:

Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS.

Conclusions:

These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

Involvement of cortico-efferent tracts in flail arm syndrome: a tract-of-interest-based DTI study

Background

Flail arm syndrome is a restricted phenotype of motor neuron disease that is characterized by progressive, predominantly proximal weakness and atrophy of the upper limbs.

Objective

The study was designed to investigate specific white matter alterations in diffusion tensor imaging (DTI) data from flail arm syndrome patients using a hypothesis-guided tract-of-interest-based approach to identify in vivo microstructural changes according to a neuropathologically defined amyotrophic lateral sclerosis (ALS)-related pathology of the cortico-efferent tracts.

Methods

DTI-based white matter mapping was performed both by an unbiased voxel-wise statistical comparison and by a hypothesis-guided tract-wise analysis of fractional anisotropy (FA) maps according to the neuropathological ALS-propagation pattern for 43 flail arm syndrome patients vs 43 ‘classical’ ALS patients vs 40 matched controls.

Results

The analysis of white matter integrity demonstrated regional FA reductions for the flail arm syndrome group predominantly along the CST. In the tract-specific analysis according to the proposed sequential cerebral pathology pattern of ALS, the flail arm syndrome patients showed significant alterations of the specific tract systems that were identical to ‘classical’ ALS if compared to controls.

Conclusions

The DTI study including the tract-of-interest-based analysis showed a microstructural involvement pattern in the brains of flail arm syndrome patients, supporting the hypothesis that flail arm syndrome is a phenotypical variant of ALS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10854-6.

Cortical hyperexcitability: Diagnostic and pathogenic biomarker of ALS

Cortical hyperexcitability is an early and intrinsic feature of both sporadic and familial forms of amyotrophic lateral sclerosis (ALS).. Importantly, cortical hyperexcitability appears to be associated with motor neuron degeneration, possibly via an anterograde glutamate-mediated excitotoxic process, thereby forming a pathogenic basis for ALS. The presence of cortical hyperexcitability in ALS patients may be readily determined by transcranial magnetic stimulation (TMS), a neurophysiological tool that provides a non-invasive and painless method for assessing cortical function. Utilising the threshold tracking TMS technique, cortical hyperexcitability has been established as a robust diagnostic biomarker that distinguished ALS from mimicking disorders at early stages of the disease process. The present review discusses the pathophysiological and diagnostic utility of cortical hyperexcitability in ALS.

Segmental involvement of the corpus callosum in C9orf72-associated ALS: a tract of interest-based DTI study

Background:

C9orf72 hexanucleotide repeat expansions are associated with widespread cerebral alterations, including white matter alterations. However, there is lack of information on changes in commissure fibres. Diffusion tensor imaging (DTI) can identify amyotrophic lateral sclerosis (ALS)-associated patterns of regional brain alterations at the group level. The objective of this study was to investigate the structural connectivity of the corpus callosum (CC) in ALS patients with C9orf72 expansions.

Methods:

DTI-based white matter mapping was performed by a hypothesis-guided tractwise analysis of fractional anisotropy (FA) maps for 25 ALS patients with C9orf72 expansion versus 25 matched healthy controls. Furthermore, a comparison with a patient control group of 25 sporadic ALS patients was performed. DTI-based tracts that originate from callosal sub-areas I to V were identified and correlated with clinical data.

Results:

The analysis of white matter integrity demonstrated regional FA reductions for tracts of the callosal areas II and III for ALS patients with C9orf72 expansions while FA reductions in sporadic ALS patients were observed only for tracts of the callosal area III; these reductions were correlated with clinical parameters.

Conclusion:

The tract-of-interest-based analysis showed a microstructural callosal involvement pattern in C9orf72-associated ALS that included the motor segment III together with frontal callosal connections, as an imaging signature of the C9orf72-associated overlap of motor neuron disease and frontotemporal pathology.

Imaging Neurochemistry and Brain Structure Tracks Clinical Decline and Mechanisms of ALS in Patients

Background: Oxidative stress and protein aggregation are key mechanisms in amyotrophic lateral sclerosis (ALS) disease. Reduced glutathione (GSH) is the most important intracellular antioxidant that protects neurons from reactive oxygen species. We hypothesized that levels of GSH measured by MR spectroscopic imaging (MRSI) in the motor cortex and corticospinal tract are linked to clinical trajectory of ALS patients.

Objectives: Investigate the value of GSH imaging to probe clinical decline of ALS patients in combination with other neurochemical and structural parameters.

Methods: Twenty-four ALS patients were imaged at 3 T with an advanced MR protocol. Mapping GSH levels in the brain is challenging, and for this purpose, we used an optimized spectral-edited 3D MRSI sequence with real-time motion and field correction to image glutathione and other brain metabolites. In addition, our imaging protocol included (i) an adiabatic T1ρ sequence to image macromolecular fraction of brain parenchyma, (ii) diffusion tensor imaging (DTI) for white matter tractography, and (iii) high-resolution anatomical imaging.

Results: We found GSH in motor cortex (r = −0.431, p = 0.04) and corticospinal tract (r = −0.497, p = 0.016) inversely correlated with time between diagnosis and imaging. N-Acetyl-aspartate (NAA) in motor cortex inversely correlated (r = −0.416, p = 0.049), while mean water diffusivity (r = 0.437, p = 0.033) and T1ρ (r = 0.482, p = 0.019) positively correlated with disease progression measured by imputed change in revised ALS Functional Rating Scale. There is more decrease in the motor cortex than in the white matter for GSH compared to NAA, glutamate, and glutamine.

Conclusions: Our study suggests that a panel of biochemical and structural imaging biomarkers defines a brain endophenotype, which can be used to time biological events and clinical progression in ALS patients. Such a quantitative brain endophenotype may stratify ALS patients into more homogeneous groups for therapeutic interventions compared to clinical criteria.

In vivo histopathological staging in C9orf72-associated ALS: A tract of interest DTI study

BACKGROUND

Diffusion tensor imaging (DTI) can identify amyotrophic lateral sclerosis (ALS)-associated patterns of brain alterations at the group level according to a neuropathological staging system.

OBJECTIVE

The study was designed to investigate the in vivo staging in ALS patients with the C9orf72 expansion and potential differences to ALS patients with the SOD1 mutation.

METHODS

DTI-based white matter mapping was performed both by an unbiased voxel-wise statistical comparison and by a hypothesis-guided tract-wise analysis of fractional anisotropy (FA) maps according to the ALS-staging pattern for 27 ALS patients with C9orf72 expansion vs 15 ALS patients with SOD1 mutation vs 32 matched healthy controls. Clinical and neuropsychological data were acquired and correlated to DTI data.

RESULTS

The analysis of white matter integrity demonstrated regional FA reductions along the CST and also in frontal and prefrontal brain areas according to the proposed propagation pattern for the ALS patients with C9orf72 expansion and sporadic patients. This pattern could not be identified for the SOD1 mutation at the group level. In contrast, in the tract-specific analysis according to the neuropathological ALS-staging pattern, C9orf72 expansion ALS patients showed significant alterations of ALS-related tract systems similar to sporadic patients.

CONCLUSIONS

The DTI study including the tract-of-interest-based analysis showed a microstructural corticoefferent involvement pattern according to the staging scheme in C9orf72-associated ALS patients but not in the SOD1 mutation.