Disease aggressiveness signatures of amyotrophic lateral sclerosis in white matter tracts revealed by the D50 disease progression model

T1-weighted MRI texture analysis in amyotrophic lateral sclerosis patients stratified by the D50 progression model

Amyotrophic lateral sclerosis, a progressive neurodegenerative disease, presents challenges in predicting individual disease trajectories due to its heterogeneous nature. This study explores the application of texture analysis on T1-weighted MRI in patients with amyotrophic lateral sclerosis, stratified by the D50 disease progression model. The D50 model, which offers a more nuanced representation of disease progression than traditional linear metrics, calculates the sigmoidal curve of functional decline and provides independent quantifications of disease aggressiveness and accumulation. In this research, a representative cohort of 116 patients with amyotrophic lateral sclerosis was studied using the D50 model and texture analysis on MRI images. Texture analysis, a technique used for quantifying voxel intensity patterns in MRI images, was employed to discern alterations in brain tissue associated with amyotrophic lateral sclerosis. This study examined alterations of the texture feature autocorrelation across sub-groups of patients based on disease accumulation, aggressiveness and the first site of onset, as well as in direct regressions with accumulation/aggressiveness. The findings revealed distinct patterns of the texture-derived autocorrelation in grey and white matter, increase in bilateral corticospinal tract, right hippocampus and left temporal pole as well as widespread decrease within motor and extra-motor brain regions, of patients stratified based on their disease accumulation. Autocorrelation alterations in grey and white matter, in clusters within the left cingulate gyrus white matter, brainstem, left cerebellar tonsil grey matter and right inferior fronto-occipital fasciculus, were also negatively associated with disease accumulation in regression analysis. Otherwise, disease aggressiveness correlated with only two small clusters, within the right superior temporal gyrus and right posterior division of the cingulate gyrus white matter. The findings suggest that texture analysis could serve as a potential biomarker for disease stage in amyotrophic lateral sclerosis, with potential for quick assessment based on using T1-weighted images.

Diffusion tensor imaging biomarkers and clinical assessments in amyotrophic lateral sclerosis (ALS) patients: an exploratory study

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of upper and lower motor neurons. Biomarkers are needed to improve diagnosis, gauge progression, and evaluate treatment. Diffusion tensor imaging (DTI) is a promising biomarker for detecting microstructural alterations in the white matter tracts. This study aimed to assess DTI metrics as biomarkers and to examine their relationship with clinical assessments in patients with ALS. Eleven patients with ALS and 21 healthy controls (HCs) underwent 3T MRI with DTI. DTI metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), were compared between key motor and extra-motor tract groups. Group comparisons and correlations between DTI metrics also correlated with clinical scores of disability (ALSFRS-R), muscle strength (dynamometry), and motor unit loss (MUNIX). Widespread differences were found between patients with ALS and HCs in DTI metrics, including decreased FA and increased diffusivity metrics. However, MD and RD are more sensitive metrics for detecting white matter changes in patients with ALS. Significant interhemispheric correlations between the tract DTI metrics were also observed. DTI metrics showed symmetry between the hemispheres and correlated with the clinical assessments. MD, RD, and AD increases significantly correlated with lower ALSFRS-R and MUNIX scores and weaker dynamometry results. DTI reveals microstructural damage along the motor and extra-motor regions in ALS patients. DTI metrics can serve as quantitative neuroimaging biomarkers for diagnosis, prognosis, monitoring of progression, and treatment. Combined analysis of imaging, electrodiagnostic, and functional biomarkers shows potential for characterizing disease pathophysiology and progression.

Novel approaches to assessing upper motor neuron dysfunction in motor neuron disease/amyotrophic lateral sclerosis: IFCN handbook chapter

Identifying upper motor neuron (UMN) dysfunction is fundamental to the diagnosis and understanding of disease pathogenesis in motor neuron disease (MND). The clinical assessment of UMN dysfunction may be difficult, particularly in the setting of severe muscle weakness. From a physiological perspective, transcranial magnetic stimulation (TMS) techniques provide objective biomarkers of UMN dysfunction in MND and may also be useful to interrogate cortical and network function. Single, paired- and triple pulse TMS techniques have yielded novel diagnostic and prognostic biomarkers in MND, and have provided important pathogenic insights, particularly pertaining to site of disease onset. Cortical hyperexcitability, as heralded by reduced short interval intracortical inhibition (SICI) and increased short interval intracortical facilitation, has been associated with the onset of lower motor neuron degeneration, along with patterns of disease spread, development of specific clinical features such as the split hand phenomenon, and may provide an indication about the rate of disease progression. Additionally, reduction of SICI has emerged as a potential diagnostic aid in MND. The triple stimulation technique (TST) was shown to enhance the diagnostic utility of conventional TMS measures in detecting UMN dysfunction in MND. Separately, sophisticated brain imaging techniques have uncovered novel biomarkers of neurodegeneration that have bene associated with progression. The present review will discuss the utility of TMS and brain neuroimaging derived biomarkers of UMN dysfunction in MND, focusing on recently developed TMS techniques and advanced neuroimaging modalities that interrogate structural and functional integrity of the corticomotoneuronal system, with an emphasis on pathogenic, diagnostic, and prognostic utility.

Trajectories and contributing factors of neural compensation in healthy and pathological aging

Neural degeneration is a hallmark of healthy aging and can be associated with specific cognitive impairments. However, neural degeneration per se is not matched by unremitting declines in cognitive abilities. Instead, middle-aged and older adults typically maintain surprisingly high levels of cognitive functioning, suggesting that the human brain can adapt to structural degeneration by neural compensation. Here, we summarize prevailing theories and recent empirical studies on neural compensation with a focus on often neglected contributing factors, such as lifestyle, metabolism and neural plasticity. We suggest that these factors moderate the relationship between structural integrity and neural compensation, maintaining psychological well-being and behavioral functioning. Finally, we discuss that a breakdown in neural compensation may pose a tipping point that distinguishes the trajectories of healthy vs pathological aging, but conjoint support from psychology and cognitive neuroscience for this alluring view is still scarce. Therefore, future experiments that target the concomitant processes of neural compensation and associated behavior will foster a comprehensive understanding of both healthy and pathological aging.

Association between brain and upper cervical spinal cord atrophy assessed by MRI and disease aggressiveness in amyotrophic lateral sclerosis

PURPOSE

To study the relative contributions of brain and upper cervical spinal cord compartmental atrophy to disease aggressiveness in amyotrophic lateral sclerosis (ALS).

METHODS

Twenty-nine ALS patients and 24 age- and gender-matched healthy controls (HC) were recruited. Disease duration and the Revised-ALS Functional Rating Scale (ALSFRS-R) at baseline, 3- and 6-months follow-up were assessed. Patients were clinically differentiated into fast (n=13) and slow (n=16) progressors according to their ALSFRS-R progression rate. Brain grey (GM) and white matter, brainstem sub-structures volumes and spinal cord cross-sectional area (SC-CSA) at C1-C2 vertebral levels were measured from a 3D-T1-weighted MRI.

RESULTS

Fast progressors showed significant GM, medulla oblongata and SC atrophy compared to HC (p<0.001, p=0.013 and p=0.008) and significant GM atrophy compared to slow progressors (p=0.008). GM volume correlated with the ALSFRS-R progression rate (Rho/p=-0.487/0.007), the ALSFRS-R at 3-months (Rho/p=0.622/0.002), and ALSFRS-R at 6-months (Rho/p=0.407/0.039). Medulla oblongata volume and SC-CSA correlated with the ALSFRS-R at 3-months (Rho/p=0.510/0.015 and Rho/p=0.479/0.024). MRI measures showed high performance to discriminate between fast and slow progressors.

CONCLUSION

Our study suggests an association between compartmental atrophy and disease aggressiveness. This result is consistent with the combination of upper and lower motor neuron degeneration as the main driver of disease worsening and severity in ALS. Our study highlights the potential of brain and spinal cord atrophy measured by MRI as biomarker of disease aggressiveness signature.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.

OBJECTIVE

This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.

METHODS

We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.

RESULTS

Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.

CONCLUSION

This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Motor unit number index (MUNIX) loss of 50% occurs in half the time of 50% functional loss according to the D50 disease progression model of ALS

Capturing disease progression in amyotrophic lateral sclerosis (ALS) is challenging and refinement of progression markers is urgently needed. This study introduces new motor unit number index (MUNIX), motor unit size index (MUSIX) and compound muscle action potential (CMAP) parameters called M50, MUSIX200 and CMAP50. M50 and CMAP50 indicate the time in months from symptom onset an ALS patient needs to lose 50% of MUNIX or CMAP in relation to the mean values of controls. MUSIX200 represents the time in months until doubling of the mean MUSIX of controls. We used MUNIX parameters of Musculi abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) of 222 ALS patients. Embedded in the D50 disease progression model, disease aggressiveness and accumulation were analyzed separately. M50, CMAP50 and MUSIX200 significantly differed among disease aggressiveness subgroups (p < 0.001) regardless of disease accumulation. ALS patients with a low M50 had a significantly shorter survival compared to high M50 (median 32 versus 74 months). M50 preceded the loss of global function (median of about 14 months). M50, CMAP50 and MUSIX200 characterize the disease course in ALS in a new way and may be applied as early measures of disease progression.

Motor unit number index (MUNIX) in the D50 disease progression model reflects disease accumulation independently of disease aggressiveness in ALS

The neurophysiological technique motor unit number index (MUNIX) is increasingly used in clinical trials to measure loss of motor units. However, the heterogeneous disease course in amyotrophic lateral sclerosis (ALS) obfuscates robust correlations between clinical status and electrophysiological assessments. To address this heterogeneity, MUNIX was applied in the D50 disease progression model by analyzing disease aggressiveness (D50) and accumulation (rD50 phase) in ALS separately. 237 ALS patients, 45 controls and 22 ALS-Mimics received MUNIX of abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) muscles. MUNIX significantly differed between controls and ALS patients and between ALS-Mimics and controls. Within the ALS cohort, significant differences between Phase I and II revealed in MUNIX, compound muscle action potential (CMAP) and motor unit size index (MUSIX) of APB as well as in MUNIX and CMAP of TA. For the ADM, significant differences occurred later in CMAP and MUNIX between Phase II and III/IV. In contrast, there was no significant association between disease aggressiveness and MUNIX. In application of the D50 disease progression model, MUNIX can demonstrate disease accumulation already in early Phase I and evaluate effects of therapeutic interventions in future therapeutic trials independent of individual disease aggressiveness.

Cortical and subcortical grey matter atrophy in Amyotrophic Lateral Sclerosis correlates with measures of disease accumulation independent of disease aggressiveness

There is a growing demand for reliable biomarkers to monitor disease progression in Amyotrophic Lateral Sclerosis (ALS) that also take the heterogeneity of ALS into account. In this study, we explored the association between Magnetic Resonance Imaging (MRI)-derived measures of cortical thickness (CT) and subcortical grey matter (GM) volume with D50 model parameters. T1-weighted MRI images of 72 Healthy Controls (HC) and 100 patients with ALS were analyzed using Surface-based Morphometry for cortical structures and Voxel-based Morphometry for subcortical Region-Of-Interest analyses using the Computational Anatomy Toolbox (CAT12). In Inter-group contrasts, these parameters were compared between patients and HC. Further, the D50 model was used to conduct subgroup-analyses, dividing patients by a) Phase of disease covered at the time of MRI-scan and b) individual overall disease aggressiveness. Finally, correlations between GM and D50 model-derived parameters were examined. Inter-group analyses revealed ALS-related cortical thinning compared to HC located mainly in frontotemporal regions and a decrease in GM volume in the left hippocampus and amygdala. A comparison of patients in different phases showed further cortical and subcortical GM atrophy along with disease progression. Correspondingly, regression analyses identified negative correlations between cortical thickness and individual disease covered. However, there were no differences in CT and subcortical GM between patients with low and high disease aggressiveness. By application of the D50 model, we identified correlations between cortical and subcortical GM atrophy and ALS-related functional disability, but not with disease aggressiveness. This qualifies CT and subcortical GM volume as biomarkers representing individual disease covered to monitor therapeutic interventions in ALS.

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.

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics

Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of 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.

Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a relentless neurodegenerative disease of the human motor neuron system, where variability in progression rate limits clinical trial efficacy. Therefore, better prognostication will facilitate therapeutic progress. In this study, we investigated the potential of plasma cell-free microRNAs (miRNAs) as ALS prognostication biomarkers in 252 patients with detailed clinical phenotyping. First, we identified, in a longitudinal cohort, miRNAs whose plasma levels remain stable over the course of disease. Next, we showed that high levels of miR-181, a miRNA enriched in neurons, predicts a greater than two-fold risk of death in independent discovery and replication cohorts (126 and 122 patients, respectively). miR-181 performance is similar to neurofilament light chain (NfL), and when combined together, miR-181 + NfL establish a novel RNA-protein biomarker pair with superior prognostication capacity. Therefore, plasma miR-181 alone and a novel miRNA-protein biomarker approach, based on miR-181 + NfL, boost precision of patient stratification. miR-181-based ALS biomarkers encourage additional validation and might enhance the power of clinical trials.

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

Background

Longitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualization of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans.

Methods

Different analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington’s disease patients, detected by atlas-based volumetry (ABV).

Results

For the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis.

Conclusions

This theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.

Patterns of grey and white matter changes differ between bulbar and limb onset amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that is characterized by a high heterogeneity in patients' disease course. Patients with bulbar onset of symptoms (b-ALS) have a poorer prognosis than patients with limb onset (l-ALS). However, neuroimaging correlates of the assumed biological difference between b-ALS and l-ALS may have been obfuscated by patients' diversity in the disease course. We conducted Voxel-Based-Morphometry (VBM) and Tract-Based-Spatial-Statistics (TBSS) in a group of 76 ALS patients without clinically relevant cognitive deficits. The subgroups of 26 b-ALS and 52 l-ALS patients did not differ in terms of disease Phase or disease aggressiveness according to the D50 progression model. VBM analyses showed widespread ALS-related changes in grey and white matter, that were more pronounced for b-ALS. TBSS analyses revealed that b-ALS was predominantly characterized by frontal fractional anisotropy decreases. This demonstrates a higher degree of neurodegenerative burden for the group of b-ALS patients in comparison to l-ALS. Correspondingly, higher bulbar symptom burden was associated with right-temporal and inferior-frontal grey matter density decreases as well as fractional anisotropy decreases in inter-hemispheric and long association tracts. Contrasts between patients in Phase I and Phase II further revealed that b-ALS was characterized by an early cortical pathology and showed a spread only outside primary motor regions to frontal and temporal areas. In contrast, l-ALS showed ongoing structural integrity loss within primary motor-regions until Phase II. We therefore provide a strong rationale to treat both onset types of disease separately in ALS studies.

Cerebrospinal Fluid Neurofilament Light Chain (NfL) Predicts Disease Aggressiveness in Amyotrophic Lateral Sclerosis: An Application of the D50 Disease Progression Model

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disorder. As previous therapeutic trials in ALS have been severely hampered by patients’ heterogeneity, the identification of biomarkers that reliably reflect disease progression represents a priority in ALS research. Here, we used the D50 disease progression model to investigate correlations between cerebrospinal fluid (CSF) neurofilament light chain (NfL) levels and disease aggressiveness. The D50 model quantifies individual disease trajectories for each ALS patient. The value D50 provides a unified measure of a patient’s overall disease aggressiveness (defined as time taken in months to lose 50% of functionality). The relative D50 (rD50) reflects the individual disease covered and can be calculated for any time point in the disease course. We analyzed clinical data from a well-defined cohort of 156 patients with ALS. The concentration of NfL in CSF samples was measured at two different laboratories using the same procedure. Based on patients’ individual D50 values, we defined subgroups with high (<20), intermediate (20–40), or low (>40) disease aggressiveness. NfL levels were compared between these subgroups via analysis of covariance, using an array of confounding factors: age, gender, clinical phenotype, frontotemporal dementia, rD50-derived disease phase, and analyzing laboratory. We found highly significant differences in NfL concentrations between all three D50 subgroups (p < 0.001), representing an increase of NfL levels with increasing disease aggressiveness. The conducted analysis of covariance showed that this correlation was independent of gender, disease phenotype, and phase; however, age, analyzing laboratory, and dementia significantly influenced NfL concentration. We could show that CSF NfL is independent of patients’ disease covered at the time of sampling. The present study provides strong evidence for the potential of NfL to reflect disease aggressiveness in ALS and in addition proofed to remain at stable levels throughout the disease course. Implementation of CSF NfL as a potential read-out for future therapeutic trials in ALS is currently constrained by its demonstrated susceptibility to (pre-)analytical variations. Here we show that the D50 model enables the discovery of correlations between clinical characteristics and CSF analytes and can be recommended for future studies evaluating potential biomarkers.

Disease aggressiveness signatures of amyotrophic lateral sclerosis in white matter tracts revealed by the D50 disease progression model

Numerous neuroimaging studies in amyotrophic lateral sclerosis (ALS) have reported links between structural changes and clinical data; however phenotypic and disease course heterogeneity have occluded robust associations. The present study used the novel D50 model, which distinguishes between disease accumulation and aggressiveness, to probe correlations with measures of diffusion tensor imaging (DTI). DTI scans of 145 ALS patients and 69 controls were analyzed using tract-based-spatial-statistics of fractional anisotropy (FA), mean- (MD), radial (RD), and axial diffusivity (AD) maps. Intergroup contrasts were calculated between patients and controls, and between ALS subgroups: based on (a) the individual disease covered (Phase I vs. II) or b) patients' disease aggressiveness (D50 value). Regression analyses were used to probe correlations with model-derived parameters. Case-control comparisons revealed widespread ALS-related white matter pathology with decreased FA and increased MD/RD. These affected pathways showed also correlations with the accumulated disease for increased MD/RD, driven by the subgroup of Phase I patients. No significant differences were noted between patients in Phase I and II for any of the contrasts. Patients with high disease aggressiveness (D50 < 30 months) displayed increased AD/MD in bifrontal and biparietal pathways, which was corroborated by significant voxel-wise regressions with D50. Application of the D50 model revealed associations between DTI measures and ALS pathology in Phase I, representing individual disease accumulation early in disease. Patients' overall disease aggressiveness correlated robustly with the extent of DTI changes. We recommend the D50 model for studies developing/validating neuroimaging or other biomarkers for ALS.