Neuroimaging in amyotrophic lateral sclerosis: current and emerging uses

Subcortical grey matter involvement in ALS and PLS - vulnerable hubs of cortico-cortical and cortico-basal circuits: extrapyramidal, cognitive, bulbar and respiratory correlates

Evidence from neuroimaging studies suggests that the cardinal clinical manifestations of ALS stem from the dysfunction of specific neural networks. The majority of cortico-cortical and cortico-basal networks are physiologically relayed by deep cerebral and cerebellar grey matter nuclei which have been increasingly implicated in the pathophysiology of ALS. A series of recent human imaging papers revealed volume reductions, shape deformations, metabolic alterations and more recently, susceptibility changes in hippocampal subfields, thalamic, striatal, amygdalar and cerebellar nuclei. Thalamic changes have been identified in presymptomatic mutation carriers long before symptom onset and longitudinal studies have consistently confirmed progressive subcortical degeneration during the symptomatic phase of the disease. The dysfunction of circuits relayed by specific subcortical nuclei has been associated with apathy, amnestic deficits, limbic symptoms, extrapyramidal manifestations, sensory disturbances, pseudobulbar affect and cerebellar deficits. In light of emerging imaging data, the clinical heterogeneity of ALS is probably best approached from a network integrity perspective. Accordingly, the comprehensive assessment of subcortical grey matter nuclei seems imperative to untangle complex clinical phenomena in ALS.

Unraveling the complexity of human brain: Structure, function in healthy and disease states

The human brain stands as an intricate organ, embodying a nexus of structure, function, development, and diversity. This review delves into the multifaceted landscape of the brain, spanning its anatomical intricacies, diverse functional capacities, dynamic developmental trajectories, and inherent variability across individuals. The dynamic process of brain development, from early embryonic stages to adulthood, highlights the nuanced changes that occur throughout the lifespan. The brain, a remarkably complex organ, is composed of various anatomical regions, each contributing uniquely to its overall functionality. Through an exploration of neuroanatomy, neurophysiology, and electrophysiology, this review elucidates how different brain structures interact to support a wide array of cognitive processes, sensory perception, motor control, and emotional regulation. Moreover, it addresses the impact of age, sex, and ethnic background on brain structure and function, and gender differences profoundly influence the onset, progression, and manifestation of brain disorders shaped by genetic, hormonal, environmental, and social factors. Delving into the complexities of the human brain, it investigates how variations in anatomical configuration correspond to diverse functional capacities across individuals. Furthermore, it examines the impact of neurodegenerative diseases on the structural and functional integrity of the brain. Specifically, our article explores the pathological processes underlying neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, shedding light on the structural alterations and functional impairments that accompany these conditions. We will also explore the current research trends in neurodegenerative diseases and identify the existing gaps in the literature. Overall, this article deepens our understanding of the fundamental principles governing brain structure and function and paves the way for a deeper understanding of individual differences and tailored approaches in neuroscience and clinical practice-additionally, a comprehensive understanding of structural and functional changes that manifest in neurodegenerative diseases.

Association Between Hypothalamic Volume and Metabolism, Cognition, and Behavior in Patients With Amyotrophic Lateral Sclerosis

BACKGROUND AND OBJECTIVES

Dysfunction of energy metabolism, cognition, and behavior are important nonmotor symptoms of amyotrophic lateral sclerosis (ALS), negatively affecting survival and quality of life, but poorly understood. Neuroimaging is ideally suited to studying nonmotor neurodegeneration in ALS, but few studies have focused on the hypothalamus, a key region for regulating energy homeostasis, cognition, and behavior. We evaluated, therefore, hypothalamic neurodegeneration in ALS and explored the relationship between hypothalamic volumes and dysregulation of energy metabolism, cognitive and behavioral changes, disease progression, and survival.

METHODS

Patients with ALS and population-based controls were included for this cross-sectional and longitudinal MRI study. The hypothalamus was segmented into 5 subregions and their volumes were calculated. Linear (mixed) models, adjusted for age, sex, and total intracranial volume, were used to compare hypothalamic volumes between groups and to analyze associations with metabolism, cognition, behavior, and disease progression. Cox proportional hazard models were used to investigate the relationship of hypothalamic volumes with survival. Permutation-based corrections for multiple hypothesis testing were applied to all analyses to control the family-wise error rate.

RESULTS

Data were available for 564 patients with ALS and 356 controls. The volume of the anterior superior subregion of the hypothalamus was smaller in patients with ALS than in controls (β = -0.70 [-1.15 to -0.25], p = 0.013). Weight loss, memory impairments, and behavioral disinhibition were associated with a smaller posterior hypothalamus (β = -4.79 [-8.39 to -2.49], p = 0.001, β = -10.14 [-15.88 to -4.39], p = 0.004, and β = -12.09 [-18.83 to -5.35], p = 0.003, respectively). Furthermore, the volume of this subregion decreased faster over time in patients than in controls (β = -0.25 [0.42 to -0.09], p = 0.013), and a smaller volume of this structure was correlated with shorter survival (hazard ratio = 0.36 [0.21-0.61], p = 0.029).

DISCUSSION

We obtained evidence for hypothalamic involvement in ALS, specifically marked by atrophy of the anterior superior subregion. Moreover, we found that atrophy of the posterior hypothalamus was associated with weight loss, memory dysfunction, behavioral disinhibition, and survival, and that this subregion deteriorated faster in patients with ALS than in controls. These findings improve our understanding of nonmotor involvement in ALS and could contribute to the identification of new treatment targets for this devastating disease.

Limbic Network and Papez Circuit Involvement in ALS: Imaging and Clinical Profiles in GGGGCC Hexanucleotide Carriers in C9orf72 and C9orf72-Negative Patients

Background: While frontotemporal involvement is increasingly recognized in Amyotrophic lateral sclerosis (ALS), the degeneration of limbic networks remains poorly characterized, despite growing evidence of amnestic deficits, impaired emotional processing and deficits in social cognition. Methods: A prospective neuroimaging study was conducted with 204 individuals with ALS and 111 healthy controls. Patients were stratified for hexanucleotide expansion status in C9orf72. A deep-learning-based segmentation approach was implemented to segment the nucleus accumbens, hypothalamus, fornix, mammillary body, basal forebrain and septal nuclei. The cortical, subcortical and white matter components of the Papez circuit were also systematically evaluated. Results: Hexanucleotide repeat expansion carriers exhibited bilateral amygdala, hypothalamus and nucleus accumbens atrophy, and C9orf72 negative patients showed bilateral basal forebrain volume reductions compared to controls. Both patient groups showed left rostral anterior cingulate atrophy, left entorhinal cortex thinning and cingulum and fornix alterations, irrespective of the genotype. Fornix, cingulum, posterior cingulate, nucleus accumbens, amygdala and hypothalamus degeneration was more marked in C9orf72-positive ALS patients. Conclusions: Our results highlighted that mesial temporal and parasagittal subcortical degeneration is not unique to C9orf72 carriers. Our radiological findings were consistent with neuropsychological observations and highlighted the importance of comprehensive neuropsychological testing in ALS, irrespective of the underlying genotype.

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.

Distinct neural signatures of pulvinar in C9orf72 amyotrophic lateral sclerosis mutation carriers and noncarriers

BACKGROUND AND PURPOSE

Thalamic alterations have been reported as a major feature in presymptomatic and symptomatic patients carrying the C9orf72 mutation across the frontotemporal dementia-amyotrophic lateral sclerosis (ALS) spectrum. Specifically, the pulvinar, a high-order thalamic nucleus and timekeeper for large-scale cortical networks, has been hypothesized to be involved in C9orf72-related neurodegenerative diseases. We investigated whether pulvinar volume can be useful for differential diagnosis in ALS C9orf72 mutation carriers and noncarriers and how underlying functional connectivity changes affect this region.

METHODS

We studied 19 ALS C9orf72 mutation carriers (ALSC9+) accurately matched with wild-type ALS (ALSC9-) and ALS mimic (ALSmimic) patients using structural and resting-state functional magnetic resonance imaging data. Pulvinar volume was computed using automatic segmentation. Seed-to-voxel functional connectivity analyses were performed using seeds from a pulvinar functional parcellation.

RESULTS

Pulvinar structural integrity had high discriminative values for ALSC9+ patients compared to ALSmimic (area under the curve [AUC] = 0.86) and ALSC9- (AUC = 0.77) patients, yielding a volume cutpoint of approximately 0.23%. Compared to ALSmimic, ALSC9- showed increased anterior, inferior, and lateral pulvinar connections with bilateral occipital-temporal-parietal regions, whereas ALSC9+ showed no differences. ALSC9+ patients when compared to ALSC9- patients showed reduced pulvinar-occipital connectivity for anterior and inferior pulvinar seeds.

CONCLUSIONS

Pulvinar volume could be a differential biomarker closely related to the C9orf72 mutation. A pulvinar-cortical circuit dysfunction might play a critical role in disease progression and development, in both the genetic phenotype and ALS wild-type patients.

Cognitive function in amyotrophic lateral sclerosis: a cross-sectional and prospective pragmatic clinical study with review of the literature

BACKGROUND

Amyotrophic lateral sclerosis (ALS) can present with either bulbar or spinal symptoms, and in some cases, both types of symptoms may be present. In addition, cognitive impairment has been observed in ALS. The study aimed to evaluate the frontal and general cognitive performance in ALS not only cross-sectionally but also longitudinally.

METHODS AND MATERIALS

The Frontal Assessment Battery (FAB) and the Montreal Cognitive Assessment (MoCA) were employed to assess cognitive function in 52 adults with ALS and 52 cognitively healthy individuals. The statistical analyses encompassed the Pearson Chi square test, the Skillings-Mack test, the Spearman's rank correlation coefficient, and the Proportional Odds Logistic Regression Model (POLR).

RESULTS

Cross-sectionally, lower cognitive performance was associated with ALS diagnosis, older age, and motor functional decline. The cognitive impairment of individuals with bulbar and spinal-bulbar symptoms showed faster deterioration compared to those with spinal symptoms. The spinal subgroup consistently performed worst in delayed recall and attention, while the spinal-bulbar and bulbar subgroups exhibited inferior scores in delayed recall, attention, visuospatial skills, orientation, and verbal fluency.

CONCLUSION

The incorporation of cognitive screening in the diagnostic workup of ALS may be beneficial, as early detection can enhance symptom management and improve the quality of life for both individuals with ALS and their care partners.

Premorbid brain structure influences risk of amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a disease of the motor network associated with brain structure and functional connectivity alterations that are implicated in disease progression. Whether such changes have a causal role in ALS, fitting with a postulated influence of premorbid cerebral architecture on the phenotypes associated with neurodegenerative disorders is not known.

METHODS

This study considered causal effects and shared genetic risk of 2240 structural and functional MRI brain scan imaging-derived phenotypes (IDPs) on ALS using two sample Mendelian randomisation, with putative associations further examined with extensive sensitivity analysis. Shared genetic predisposition between IDPs and ALS was explored using genetic correlation analysis.

RESULTS

Increased white matter volume in the cerebral hemispheres was causally associated with ALS. Weaker causal associations were observed for brain stem grey matter volume, parieto-occipital white matter surface and volume of the left thalamic ventral anterior nucleus. Genetic correlation was observed between ALS and intracellular volume fraction and isotropic free water volume fraction within the posterior limb of the internal capsule.

CONCLUSIONS

This study provides evidence that premorbid brain structure, in particular white matter volume, contributes to the risk of ALS.

Differential cortical gray matter changes in early- and late-onset patients with amyotrophic lateral sclerosis

Age at onset may be an important feature associated with distinct subtypes of amyotrophic lateral sclerosis (ALS). Little is known about the neuropathological mechanism of early-onset ALS (EO-ALS) and late-onset ALS (LO-ALS). Ninety ALS patients were divided into EO-ALS and LO-ALS group, and 128 healthy controls were matched into young controls(YCs) and old controls (OCs). A voxel-based morphometry approach was employed to investigate differences in gray matter volume (GMV). Significant age at onset-by-diagnosis interactions were found in the left parietal operculum, left precentral gyrus, bilateral postcentral gyrus, right occipital gyrus, and right orbitofrontal cortex. Post hoc analysis revealed a significant decrease in GMV in all affected regions of EO-ALS patients compared with YCs, with increased GMV in 5 of the 6 brain regions, except for the right orbitofrontal cortex, in LO-ALS patients compared with OCs. LO-ALS patients had a significantly increased GMV than EO-ALS patients after removing the aging effect. Correspondingly, GMV of the left postcentral gyrus correlated with disease severity in the 2 ALS groups. Our findings suggested that the pathological mechanisms in ALS patients with different ages at onset might differ. These findings provide unique insight into the clinical and biological heterogeneity of the 2 ALS subtypes.

Genetic analyses identify brain imaging-derived phenotypes associated with the risk of amyotrophic lateral sclerosis

Brain imaging-derived phenotypes have been suggested to be associated with amyotrophic lateral sclerosis in observational studies, but whether these associations are causal remains unclear. We aimed to assess the potential bidirectional causal associations between imaging-derived phenotypes and amyotrophic lateral sclerosis using bidirectional 2-sample Mendelian randomization analyses. Summary statistics for 469 imaging-derived phenotypes (33,224 individuals) and amyotrophic lateral sclerosis (20,806 cases and 59,804 controls) were obtained from 2 large-scale genome-wide association studies of European ancestry. We used the inverse-variance weighted Mendelian randomization method in the main analysis to assess the bidirectional associations between imaging-derived phenotypes and amyotrophic lateral sclerosis, followed by several sensitivity analyses for robustness validation. In the forward Mendelian randomization analyses, we found that genetically determined high orientation dispersion index in the right cerebral peduncle was associated with the increased risk of amyotrophic lateral sclerosis (odds ratio = 1.30, 95% confidence interval = 1.16-1.45, P = 2.26 × 10-6). In addition, the reverse Mendelian randomization analysis indicated that amyotrophic lateral sclerosis had no effect on 469 imaging-derived phenotypes. Mendelian randomization-Egger regression analysis showed no directional pleiotropy for the association between high orientation dispersion index in the right cerebral peduncle and amyotrophic lateral sclerosis, and sensitivity analyses with different Mendelian randomization models further confirmed these findings. The present systematic bidirectional Mendelian randomization analysis showed that high orientation dispersion index in the right cerebral peduncle might be the potential causal mediator of amyotrophic lateral sclerosis, which may provide predictive guidance for the prevention of amyotrophic lateral sclerosis. Further studies are warranted to replicate our findings and clarify the underlying mechanisms.

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.

Iranian clinical practice guideline for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegeneration involving motor neurons. The 3-5 years that patients have to live is marked by day-to-day loss of motor and sometimes cognitive abilities. Enormous amounts of healthcare services and resources are necessary to support patients and their caregivers during this relatively short but burdensome journey. Organization and management of these resources need to best meet patients' expectations and health system efficiency mandates. This can only occur in the setting of multidisciplinary ALS clinics which are known as the gold standard of ALS care worldwide. To introduce this standard to the care of Iranian ALS patients, which is an inevitable quality milestone, a national ALS clinical practice guideline is the necessary first step. The National ALS guideline will serve as the knowledge base for the development of local clinical pathways to guide patient journeys in multidisciplinary ALS clinics. To this end, we gathered a team of national neuromuscular experts as well as experts in related specialties necessary for delivering multidisciplinary care to ALS patients to develop the Iranian ALS clinical practice guideline. Clinical questions were prepared in the Patient, Intervention, Comparison, and Outcome (PICO) format to serve as a guide for the literature search. Considering the lack of adequate national/local studies at this time, a consensus-based approach was taken to evaluate the quality of the retrieved evidence and summarize recommendations.

Basal ganglia alterations in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) has traditionally been associated with brain damage involving the primary motor cortices and corticospinal tracts. In the recent decades, most of the research studies in ALS have focused on extra-motor and subcortical brain regions. The aim of these studies was to detect additional biomarkers able to support the diagnosis and to predict disease progression. The involvement of the frontal cortices, mainly in ALS cases who develop cognitive and/or behavioral impairment, is amply recognized in the field. A potential involvement of fronto-temporal and fronto-striatal connectivity changes in the disease evolution has also been reported. On this latter regard, there is still a shortage of studies which investigated basal ganglia (BG) alterations and their role in ALS clinical manifestation and progression. The present review aims to provide an overview on the magnetic resonance imaging studies reporting structural and/or functional BG alterations in patients with ALS, to clarify the role of BG damage in the disease clinical evolution and to propose potential future developments in this field.

Sequential alterations in diffusion metrics as correlates of disease severity in amyotrophic lateral sclerosis

BACKGROUND AND OBJECTIVE

The neuropathology of amyotrophic lateral sclerosis (ALS) follows a regional distribution pattern in the brain with four stages. Using diffusion tensor imaging (DTI), this pattern can be translated into a tract-based staging scheme to assess cerebral progression in vivo. This study investigates the association between the sequential alteration pattern and disease severity in patients with ALS.

METHODS

DTI data of 325 patients with ALS and 130 healthy controls were analyzed in a tract of interest (TOI)-based approach. Patients were categorized according to their ALS-FRS-R scores into groups with declining functionality. The fractional anisotropy (FA) values in the tracts associated with neuropathological stages were group-wise compared with healthy controls.

RESULTS

The FA in the tracts associated with ALS stages showed a decrease which could be related to the disease severity stratification, i.e., at the group level, the lower the ALS-FRS-R of the categorized patient group, the higher was the effect size of the stage-related tract. In the patient group with the highest ALS-FRS-R, Cohen's d showed a medium effect size in the corticospinal tract and small effect sizes in the other stage-related tracts. Overall, the lower the ALS-FRS-R of the categorized patient group the higher was the effect size of the comparison with healthy controls.

CONCLUSION

The progression of white matter alterations across tracts according to the model of sequential tract involvement is associated with clinical disease severity in patients with ALS, suggesting the use of staging-based DTI as a technical marker for disease progression.

Advances in the neuroimaging of motor disorders

Neuroimaging is a valuable adjunct to the history and examination in the evaluation of motor system disorders. Conventional imaging with computed tomography or magnetic resonance imaging depicts important anatomic information and helps to identify imaging patterns which may support diagnosis of a specific motor disorder. Advanced imaging techniques can provide further detail regarding volume, functional, or metabolic changes occurring in nervous system pathology. This chapter is an overview of the advances in neuroimaging with particular emphasis on both standard and less well-known advanced imaging techniques and findings, such as diffusion tensor imaging or volumetric studies, and their application to specific motor disorders. In addition, it provides reference to emerging imaging biomarkers in motor system disorders such as Parkinson disease, amyotrophic lateral sclerosis, and Huntington disease, and briefly reviews the neuroimaging findings in different causes of myelopathy and peripheral nerve disorders.

MRI Clustering Reveals Three ALS Subtypes With Unique Neurodegeneration Patterns

OBJECTIVE

The purpose of this study was to identify subtypes of amyotrophic lateral sclerosis (ALS) by comparing patterns of neurodegeneration using brain magnetic resonance imaging (MRI) and explore their phenotypes.

METHODS

We performed T1-weighted and diffusion tensor imaging in 488 clinically well-characterized patients with ALS and 338 control subjects. Measurements of whole-brain cortical thickness and white matter connectome fractional anisotropy were adjusted for disease-unrelated variation. A probabilistic network-based clustering algorithm was used to divide patients into subgroups of similar neurodegeneration patterns. Clinical characteristics and cognitive profiles were assessed for each subgroup. In total, 512 follow-up scans were used to validate clustering results longitudinally.

RESULTS

The clustering algorithm divided patients with ALS into 3 subgroups of 187, 163, and 138 patients. All subgroups displayed involvement of the precentral gyrus and are characterized, respectively, by (1) pure motor involvement (pure motor cluster [PM]), (2) orbitofrontal and temporal involvement (frontotemporal cluster [FT]), and (3) involvement of the posterior cingulate cortex, parietal white matter, temporal operculum, and cerebellum (cingulate-parietal-temporal cluster [CPT]). These subgroups had significantly distinct clinical profiles regarding male-to-female ratio, age at symptom onset, and frequency of bulbar symptom onset. FT and CPT revealed higher rates of cognitive impairment on the Edinburgh cognitive and behavioral ALS screen (ECAS). Longitudinally, clustering remained stable: at 90.4% of their follow-up visits, patients clustered in the same subgroup as their baseline visit.

INTERPRETATION

ALS can manifest itself in 3 main patterns of cerebral neurodegeneration, each associated with distinct clinical characteristics and cognitive profiles. Besides the pure motor and frontotemporal dementia (FTD)-like variants of ALS, a new neuroimaging phenotype has emerged, characterized by posterior cingulate, parietal, temporal, and cerebellar involvement. ANN NEUROL 2022;92:1030-1045.

Frequency and Longitudinal Course of Motor Signs in Genetic Frontotemporal Dementia

BACKGROUND AND OBJECTIVES

Frontotemporal dementia (FTD) is a highly heritable disorder. The majority of genetic cases are caused by autosomal dominant pathogenic variants in the chromosome 9 open reading frame 72 (c9orf72), progranulin (GRN), and microtubule-associated protein tau (MAPT) gene. As motor disorders are increasingly recognized as part of the clinical spectrum, the current study aimed to describe motor phenotypes caused by genetic FTD, quantify their temporal association, and investigate their regional association with brain atrophy.

METHODS

We analyzed baseline visit data of known carriers of a pathogenic variant in the c9orf72, GRN, or MAPT gene from the Genetic Frontotemporal Dementia Initiative cohort study. Principal component analysis with varimax rotation was performed to identify motor sign clusters that were compared with respect to frequency and severity between groups. Associations with cross-sectional atrophy patterns were determined using voxel-wise regression. We applied linear mixed effects models to assess whether groups differed in the association between motor signs and estimated time to symptom onset.

RESULTS

A total of 322 pathogenic variant carriers were included in the analysis: 122 c9orf72 (79 presymptomatic), 143 GRN (112 presymptomatic), and 57 MAPT (43 presymptomatic) pathogenic variant carriers. Principal component analysis revealed 5 motor clusters, which we call progressive supranuclear palsy (PSP)-like, bulbar amyotrophic lateral sclerosis (ALS)-like, mixed/ALS-like, Parkinson disease (PD) like, and corticobasal syndrome-like motor phenotypes. There was no significant group difference in the frequency of signs of different motor phenotypes. However, mixed/ALS-like motor signs were most frequent, followed by PD-like motor signs. Although the PSP-like phenotype was associated with mesencephalic atrophy, the mixed/ALS-like phenotype was associated with motor cortex and corticospinal tract atrophy. The PD-like phenotype was associated with widespread cortical and subcortical atrophy. Estimated time to onset, genetic group and their interaction influenced motor signs. In c9orf72 pathogenic variant carriers, motor signs could be detected up to 25 years before expected symptom onset.

DISCUSSION

These results indicate the presence of multiple natural clusters of motor signs in genetic FTD, each correlated with specific atrophy patterns. Their motor severity depends on time and the affected gene. These clinicogenetic associations can guide diagnostic evaluations and the design of clinical trials for new disease-modifying and preventive treatments.

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.

Mills’ syndrome is a unique entity of upper motor neuron disease with N-shaped progression: Three case reports

BACKGROUND

Mills’ syndrome is an extremely rare degenerative motor neuron disorder first described by Mills in 1900, but its nosological status is still not clear. We aimed to analyze the clinical features of Mills’ syndrome.

CASE SUMMARY

Herein, we present 3 cases with similar features as those described in Mills’ original paper and review the related literature. Our patients showed middle- and older-age onset, with only upper motor neuron symptoms evident throughout the course of the disease. Spastic hemiplegia began in the lower extremity with a unique progressive pattern.

CONCLUSION

We consider that Mills’ syndrome is a unique entity of motor neuron disorder with an N-shaped progression. Clinicians should maintain a high index of suspicion for the diagnosis of Mills’ syndrome when the onset involves lower extremity paralysis without evidence of lower motor neuron or sensory involvement.

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.

Pathophysiological Underpinnings of Extra-Motor Neurodegeneration in Amyotrophic Lateral Sclerosis: New Insights From Biomarker Studies

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) lie at opposing ends of a clinical, genetic, and neuropathological continuum. In the last decade, it has become clear that cognitive and behavioral changes in patients with ALS are more frequent than previously recognized. Significantly, these non-motor features can impact the diagnosis, prognosis, and management of ALS. Partially overlapping neuropathological staging systems have been proposed to describe the distribution of TAR DNA-binding protein 43 (TDP-43) aggregates outside the corticospinal tract. However, the relationship between TDP-43 inclusions and neurodegeneration is not absolute and other pathophysiological processes, such as neuroinflammation (with a prominent role of microglia), cortical hyperexcitability, and synaptic dysfunction also play a central role in ALS pathophysiology. In the last decade, imaging and biofluid biomarker studies have revealed important insights into the pathophysiological underpinnings of extra-motor neurodegeneration in the ALS-FTLD continuum. In this review, we first summarize the clinical and pathophysiological correlates of extra-motor neurodegeneration in ALS. Next, we discuss the diagnostic and prognostic value of biomarkers in ALS and their potential to characterize extra-motor neurodegeneration. Finally, we debate about how biomarkers could improve the diagnosis and classification of ALS. Emerging imaging biomarkers of extra-motor neurodegeneration that enable the monitoring of disease progression are particularly promising. In addition, a growing arsenal of biofluid biomarkers linked to neurodegeneration and neuroinflammation are improving the diagnostic accuracy and identification of patients with a faster progression rate. The development and validation of biomarkers that detect the pathological aggregates of TDP-43 in vivo are notably expected to further elucidate the pathophysiological underpinnings of extra-motor neurodegeneration in ALS. Novel biomarkers tracking the different aspects of ALS pathophysiology are paving the way to precision medicine approaches in the ALS-FTLD continuum. These are essential steps to improve the diagnosis and staging of ALS and the design of clinical trials testing novel disease-modifying treatments.

Pallidal functional connectivity changes are associated with disgust recognition in pure motor amyotrophic lateral sclerosis

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In cognitively normal ALS, we detected early difficulties in recognizing disgust.

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Pallidum functional connectivity (FC) alterations occur in pure-motor ALS patients.

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Reduced left pallidum-temporal FC is linked to altered disgust recognition.

In the present study, we aimed to investigate the resting-state functional connectivity (RS-FC) of the globus pallidus (GP) in patients with amyotrophic lateral sclerosis (ALS) compared to healthy controls, and the relationship between RS-FC changes and disgust recognition. Twenty-six pure-motor ALS patients and 52 healthy controls underwent RS functional MRI and a neuropsychological assessment including the Comprehensive Affect Testing System. A seed-based RS-FC analysis was performed between the left and right GP and the rest of the brain and compared between groups. Correlations between RS-FC significant changes and subjects’ performance in recognizing disgust were tested. Compared to controls, patients were significantly less able to recognize disgust. In ALS compared to controls, the seed-based analysis showed: reduced RS-FC between bilateral GP and bilateral middle and superior frontal and middle cingulate gyri, and increased RS-FC between bilateral GP and bilateral postcentral, supramarginal and superior temporal gyri and Rolandic operculum. Decreased RS-FC was further observed between left GP and left middle and inferior temporal gyri and bilateral caudate; and increased RS-FC was also shown between right GP and left lingual and fusiform gyri. In patients and controls, lower performance in recognizing disgust correlated with reduced RS-FC between left GP and left middle and inferior temporal gyri. In pure-motor ALS patients, we demonstrated altered RS-FC between GP and the rest of the brain. The reduced left pallidum-temporo-striatal RS-FC may have a role in the lower ability of patients in recognizing disgust.

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.

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.

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

Studying Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis with 7-T magnetic resonance

Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.

Magnetic Resonance Iron Imaging in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub-millimeter anatomical resolution. Conventionally, MRI T1-weighted, T2-weighted, and T2*-weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para-magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use

INTRODUCTION

The majority of imaging studies in ALS infer group-level imaging signatures from group comparisons, as opposed to estimating disease burden in individual patients. In a condition with considerable clinical heterogeneity, the characterisation of individual patterns of pathology is hugely relevant. In this study, we evaluate a strategy to track progressive cortical involvement in single patients by using subject-specific reference cohorts.

METHODS

We have interrogated a multi-timepoint longitudinal dataset of 61 ALS patients to demonstrate the utility of estimating cortical disease burden and the expansion of cerebral atrophy over time. We contrast our strategy to the gold-standard approach to gauge the advantages and drawbacks of our method. We modelled the evolution of cortical integrity in a conditional growth model, in which we accounted for age, gender, disability, symptom duration, education and handedness. We hypothesised that the variance associated with demographic variables will be successfully eliminated in our approach.

RESULTS

In our model, the only covariate which modulated the expansion of atrophy was motor disability as measured by the ALSFRS-r (t(153) = - 2.533, p = 0.0123). Using the standard approach, age also significantly influenced progression of CT change (t(153) = - 2.151, p = 0.033) demonstrating the validity and potential clinical utility of our approach.

CONCLUSION

Our strategy of estimating the extent of cortical atrophy in individual patients with ALS successfully corrects for demographic factors and captures relevant cortical changes associated with clinical disability. Our approach provides a framework to interpret single T1-weighted images in ALS and offers an opportunity to track cortical propagation patterns both at individual subject level and at cohort level.

The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development

While C9orf72-specific imaging signatures have been proposed by both ALS and FTD research groups and considerable presymptomatic alterations have also been confirmed in young mutation carriers, considerable inconsistencies exist in the literature. Accordingly, a systematic review of C9orf72-imaging studies has been performed to identify consensus findings, stereotyped shortcomings, and unique contributions to outline future directions. A formal literature review was conducted according to the STROBE guidelines. All identified papers were individually reviewed for sample size, choice of controls, study design, imaging modalities, statistical models, clinical profiling, and identified genotype-associated pathological patterns. A total of 74 imaging papers were systematically reviewed. ALS patients with GGGGCC repeat expansions exhibit relatively limited motor cortex involvement and widespread extra-motor pathology. C9orf72 positive FTD patients often show preferential posterior involvement. Reports of thalamic involvement are relatively consistent across the various phenotypes. Asymptomatic hexanucleotide repeat carriers often exhibit structural and functional changes decades prior to symptom onset. Common shortcomings included sample size limitations, lack of disease-controls, limited clinical profiling, lack of genetic testing in healthy controls, and absence of post mortem validation. There is a striking paucity of longitudinal studies and existing presymptomatic studies have not evaluated the predictive value of radiological changes with regard to age of onset and phenoconversion. With the advent of antisense oligonucleotide therapies, the meticulous characterisation of C9orf72-associated changes has gained practical relevance. Neuroimaging offers non-invasive biomarkers for future clinical trials, presymptomatic ascertainment, diagnostic and prognostic applications.

Detection of White Matter Ultrastructural Changes for Amyotrophic Lateral Sclerosis Characterization: A Diagnostic Study from Dti-Derived Data

In amyotrophic lateral sclerosis (ALS), magnetic resonance imaging (MRI) allows investigation at the microstructural level, employing techniques able to reveal white matter changes. In the current study, a diffusion tensor imaging (DTI) analysis, with a collection of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) indexes, was performed in ALS patients to correlate geno- and phenotype features with MRI data, to investigate an in-vivo correlation of different neuropathological patterns. All patients who underwent the MR-DTI analysis were retrospectively recruited. MRI scan was collected within three months from diagnosis. FA and ADC values were collected in corpus callosum (CC), corona radiata (CR), cerebral peduncle (CR), cerebellar peduncle (CbP) and corticospinal tract at posterior limb of internal capsule (CST). DTI analysis performed in the whole ALS cohort revealed significant FA reduction and ADC increase in all selected regions, as widespread changes. Moreover, we observed a higher value of FA in rCR in bulbar patients. A positive correlation between ALS Functional Rating Scale-Revised and FA in rCP was evident. In consideration of the non-invasiveness, the reliability and the easy reproducibility of the method, we believe that brain MRI with DTI analyses may represent a valid tool usable as a diagnostic marker in ALS.

Progression of brain functional connectivity and frontal cognitive dysfunction in ALS

OBJECTIVE

To investigate the progression of resting-state functional connectivity (rs-FC) changes in patients with amyotrophic lateral sclerosis (ALS) and their relationship with frontal cognitive alterations.

METHODS

This is a multicentre, observational and longitudinal study. At baseline and after six months, 25 ALS patients underwent 3D T1-weighted MRI, resting-state functional MRI (rs-fMRI), and the computerized Test of Attentional Performance (TAP). Using independent component analysis, rs-FC changes of brain networks involving connections to frontal lobes and their relationship with baseline cognitive scores and cognitive changes over time were assessed. With a seed-based approach, rs-FC longitudinal changes of the middle frontal gyrus (MFG) were also explored.

RESULTS

After six months, ALS patients showed an increased rs-FC of the left anterior cingulate, left middle frontal gyrus (MFG) and left superior frontal gyrus within the frontostriatal network, and of the left MFG, left supramarginal gyrus and right angular gyrus within the left frontoparietal network. Within the frontostriatal network, a worse baseline performance at TAP divided attention task was associated with an increased rs-FC over time in the left MFG and a worse baseline performance at the category fluency index was related with increased rs-FC over time in the left frontal superior gyrus. After six months, the seed-based rs-FC analysis of the MFG with the whole brain showed decreased rs-FC of the right MFG with frontoparietal regions in patients compared to controls.

CONCLUSIONS

Rs-FC changes in ALS patients progressed over time within the frontostriatal and the frontoparietal networks and are related to frontal-executive dysfunction. The MFG seems a potential core region in the framework of a frontoparietal functional breakdown, which is typical of frontotemporal lobar degeneration. These findings offer new potential markers for monitoring extra-motor progression in ALS.

Diagnostic and Prognostic Value of Conventional Brain MRI in the Clinical Work-Up of Patients with Amyotrophic Lateral Sclerosis

Clinical signs of upper motor neuron (UMN) involvement are important in the diagnosis of amyotrophic lateral sclerosis (ALS) though are often difficult to analyze. Many studies using both qualitative and quantitative evaluations have reported abnormal Magnetic Resonance Imaging (MRI) findings at the level of the pyramidal pathway in patients with ALS. Although the most interesting results were obtained by quantitative studies using advanced MR techniques, the qualitative evaluation of MRI images remains the most-used in clinical practice. We evaluated the diagnostic and prognostic contribution of conventional 3T-MRI in the clinical work-up of ALS patients. Two neuroradiologists retrospectively assessed 3T-MRI data of 93 ALS patients and 89 controls. The features of interest were corticospinal tract (CST) T2/FLAIR hyperintensity, motor cortex (MC) T2*/SWI hypointensity, and selective MC atrophy. All MRI features were significantly more prevalent in ALS patients than in controls. The simultaneous presence of CST FLAIR hyperintensity and MC SWI hypointensity was associated with the highest diagnostic accuracy (sensitivity: 70%; specificity: 81%; positive predictive value, PPV: 90%; negative predictive value, NPV: 51%; accuracy: 73%) and a shorter survival (HR: 6.56, p = 0.002). Conventional 3T-MRI can be a feasible tool to detect specific qualitative changes based on UMN involvement and to support clinical diagnosis of ALS. Importantly, CST FLAIR hyperintensity and MC SWI hypointensity are predictors of shorter survival in ALS patients.

Focus on the heterogeneity of amyotrophic lateral sclerosis

The clinical manifestations of amyotrophic lateral sclerosis (ALS) are variable in terms of age at disease onset, site of onset, progression of symptoms, motor neuron involvement, and the occurrence of cognitive and behavioral changes. Genetic background is a key determinant of the ALS phenotype. The mortality of the disease also varies with the ancestral origin of the affected population and environmental factors are likely to be associated with ALS at least within some cohorts. Disease heterogeneity is likely underpinned by the presence of different pathogenic mechanisms. A variety of ALS animal models can be informative about the heterogeneity of the neuropathological or genetic aspects of the disease and can support the development of new therapeutic intervention. Evolving biomarkers can contribute to the identification of differing genotypes and phenotypes, and can be used to explore whether genotypic and phenotypic differences in animal models might help to provide a better definition of the heterogeneity of ALS in humans. These include neurofilaments, peripheral blood mononuclear cells, extracellular vesicles, microRNA and imaging findings. These biomarkers might predict not only the development of the disease, but also the variability in progression, although robust validation is required. A promising area of progress in modeling the heterogeneity of human ALS is represented by the use of human induced pluripotent stem cell (iPSCs)-derived motor neurons. Although the translational value of iPSCs remains unclear, this model is attractive in the perspective of replicating the heterogeneity of sporadic ALS as a first step toward a personalized medicine strategy.

Structural MRI outcomes and predictors of disease progression in amyotrophic lateral sclerosis

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Serial diffusion tensor (DT) MRI showed progression of white matter pathology in ALS.

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Early involvement of motor fibers and later spread to extra-motor regions was found.

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DT MRI measures of damage to the motor networks showed consistent worsening.

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These correlated with clinical progression and long-term functional prognosis.

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No significant cortical thinning was detected either at baseline or over time.

Background and aims

Considering the great heterogeneity of amyotrophic lateral sclerosis (ALS), the identification of accurate prognostic predictors is fundamental for both the clinical practice and the design of treatment trials. This study aimed to explore the progression of clinical and structural brain changes in patients with ALS, and to assess magnetic resonance imaging (MRI) measures of brain damage as predictors of subsequent functional decline.

Methods

50 ALS patients underwent clinical evaluations and 3 T MRI scans at regular intervals for a maximum of 2 years (total MRI scans = 164). MRI measures of cortical thickness, as well as diffusion tensor (DT) metrics of microstructural damage along white matter (WM) tracts were obtained. Voxel-wise regression models and longitudinal mixed-effects models were used to test the relationship between clinical decline and baseline and longitudinal MRI features.

Results

The rate of decline of the ALS Functional Rating Scale revised (ALSFRS-r) was significantly associated with the rate of fractional anisotropy (FA) decrease in the body of the corpus callosum (CC). Corticospinal tract (CST) and CC-body alterations had a faster progression in patients with higher baseline ALSFRS-r scores and greater CC-body disruption at baseline. Lower FA of the cerebral peduncle was associated with faster subsequent clinical progression.

Conclusions

In this longitudinal study, we identified a significant association between measures of WM damage of the motor tracts and functional decline in ALS patients. Our data suggest that a multiparametric approach including DT MRI measures of brain damage would provide an optimal method for an accurate stratification of ALS patients into prognostic classes.

Decline of cognitive and behavioral functions in amyotrophic lateral sclerosis: a longitudinal study

Background: A cognitive impairment, ranging from frontotemporal dementia (FTD) to milder forms of dysexecutive or behavioral dysfunction, is detected in 30-50% of patients affected by amyotrophic lateral sclerosis (ALS) at diagnosis. Such condition considerably influences the prognosis, and possibly impacts on the decision-making process with regards to end-of-life choices. The aim of our study is to examine the changes of cognitive and behavioral impairment in a large population of ALS from the time of diagnosis to a 6-month follow-up (IQR 5.5-9.0 months), and to examine to what extent the progression of cognitive impairment affects survival time and rate of disease progression.Methods: We recruited 146 ALS patients classified according to revised criteria of ALS and FTD spectrum disorder. In a multidisciplinary setting, during two subsequent visits we examined clinical features with ALSFRS-r score, FVC% and BMI, and cognitive status with an extensive neuropsychological evaluation.Results: At second examination, one-third of patients showed a worsening of cognitive impairment, namely 88% of ALSbi, 27% of ALSci, 40% of ALScbi, and, interestingly, also 24% of cognitive normal ALS developed a significant cognitive dysfunction. We find that those who changed their cognitive status presented a lower ALSFRS-r score at t1 and a shorter survival time compared to those who did not change, regardless of the type of cognitive impairment.Conclusion: We show how cognitive disorders in ALS patients can not only be present at diagnosis, but also manifest during disease and influence the progression of motor deficit and the prognosis.

Cerebral atrophy in amyotrophic lateral sclerosis parallels the pathological distribution of TDP43

Amyotrophic lateral sclerosis is a neurodegenerative disease characterized by a preferential involvement of both upper and lower motor neurons. Evidence from neuroimaging and post-mortem studies confirms additional involvement of brain regions extending beyond the motor cortex. The aim of this study was to assess the extent of cerebral disease in amyotrophic lateral sclerosis cross-sectionally and longitudinally and to compare the findings with a recently proposed disease-staging model of amyotrophic lateral sclerosis pathology. Deformation-based morphometry was used to identify the patterns of brain atrophy associated with amyotrophic lateral sclerosis and to assess their relationship with clinical symptoms. Longitudinal T₁-weighted MRI data and clinical measures were acquired at baseline, 4 months and 8 months, from 66 patients and 43 age-matched controls who participated in the Canadian Amyotrophic Lateral Sclerosis Neuroimaging Consortium study. Whole brain voxel-wise mixed-effects modelling analysis showed extensive atrophy patterns differentiating patients from the normal controls. Cerebral atrophy was present in the motor cortex and corticospinal tract, involving both grey matter and white matter, and to a lesser extent in non-motor regions. More specifically, the results showed significant bilateral atrophy in the motor cortex and corticospinal tract (including the internal capsule and brainstem) and ventricular enlargement, along with significant longitudinal atrophy in precentral gyrus, frontal and parietal white matter, accompanied by ventricular and sulcal enlargement. Atrophy in the precentral gyrus was significantly associated with greater disability as quantified with the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (P < 0.0001). The pattern of atrophy observed using deformation-based morphometry was consistent with the Brettschneider’s four-stage pathological model of the disease. Deformation-based morphometry provides a sensitive indicator of atrophy in Amyotrophic lateral sclerosis and has potential as a biomarker of disease burden, in both grey matter and white matter.

Identification of Amyotrophic Lateral Sclerosis Based on Diffusion Tensor Imaging and Support Vector Machine

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.

The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases

Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.

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

Introduction: In the last decade, multiparametric magnetic resonance imaging (MRI) has achieved tremendous advances in applications to amyotrophic lateral sclerosis (ALS) to increase the understanding of the associated pathophysiology. The aim of this review is to summarize recent progress in the development of MRI-based techniques aiming to support the clinical diagnosis in ALS.Areas covered: The review of structural and functional MRI applications to ALS and its variants (restricted phenotypes) is focused on the potential of MRI techniques which contribute to the diagnostic work-up of patients with the clinical presentation of a motor neuron disease. The potential of specific MRI methods for patient diagnosis and monitoring is discussed, and the future design of clinical MRI applications to ALS is conceptualized.Expert opinion: Current multiparametric MRI allows for the use as a clinical biological marker and a technical instrument in the clinical diagnosis of patients with ALS and also of patients with ALS variants. Composite neuroimaging indices of specific anatomical areas derived from different MRI techniques might guide in the diagnostic applications to ALS. Such a development of ALS-specific MRI-based composite scores with sufficient discriminative power versus ALS mimics at an individual level requires standardized advanced protocols and comprehensive analysis approaches.

Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls

The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.

Survival prediction models in motor neuron disease

BACKGROUND AND PURPOSE

This study aimed to assess the predictive value of multimodal brain magnetic resonance imaging (MRI) on survival in a large cohort of patients with motor neuron disease (MND), in combination with clinical and cognitive features.

METHODS

Two hundred MND patients were followed up prospectively for a median of 4.13 years. At baseline, subjects underwent neurological examination, cognitive assessment and brain MRI. Grey matter volumes of cortical and subcortical structures and diffusion tensor MRI metrics of white matter tracts were obtained. A multivariable Royston-Parmar survival model was created using clinical and cognitive variables. The increase of survival prediction accuracy provided by MRI variables was assessed.

RESULTS

The multivariable clinical model included predominant upper or lower motor neuron presentations and diagnostic delay as significant prognostic predictors, reaching an area under the receiver operating characteristic curve (AUC) of a 4-year survival prediction of 0.79. The combined clinical and MRI model including selected grey matter fronto-temporal volumes and diffusion tensor MRI metrics of the corticospinal and extra-motor tracts reached an AUC of 0.89. Considering amyotrophic lateral sclerosis patients only, the clinical model including diagnostic delay and semantic fluency scores provided an AUC of 0.62, whereas the combined clinical and MRI model reached an AUC of 0.77.

CONCLUSION

Our study demonstrated that brain MRI measures of motor and extra-motor structural damage, when combined with clinical and cognitive features, are useful predictors of survival in patients with MND, particularly when a diagnosis of amyotrophic lateral sclerosis is made.

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.

Imaging Biomarkers for the Diagnosis and Prognosis of Neurodegenerative Diseases. The Example of Amyotrophic Lateral Sclerosis

The term amyotrophic lateral sclerosis (ALS) comprises a heterogeneous group of fatal neurodegenerative disorders of largely unknown etiology characterized by the upper motor neurons (UMN) and/or lower motor neurons (LMN) degeneration. The development of brain imaging biomarkers is essential to advance in the diagnosis, stratification and monitoring of ALS, both in the clinical practice and clinical trials. In this review, the characteristics of an optimal imaging biomarker and common pitfalls in biomarkers evaluation will be discussed. Moreover, the development and application of the most promising brain magnetic resonance (MR) imaging biomarkers will be reviewed. Finally, the integration of both qualitative and quantitative multimodal brain MR biomarkers in a structured report will be proposed as a support tool for ALS diagnosis and stratification.