Multicenter validation of [18F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls

Brain positron emission tomography in idiopathic normal-pressure hydrocephalus: new 18 F-fluorodeoxyglucose pattern in a long-known syndrome

AIM

Patients with idiopathic normal-pressure hydrocephalus (iNPH) can show a global reduction in cerebral glucose metabolism at [ 18 F]Fluorodeoxyglucose (FDG) PET. The presence of caudate hypometabolism has been identified as a potential biomarker in iNPH, yet there is limited evidence of hypermetabolic findings in patients with iNPH so far.

METHODS

We retrieved retrospectively patients with iNPH and normal cognitive assessment, evaluated before surgery undergoing brain [ 18 F]FDG-PET. The 18 F-FDG-PET brain scans were compared to those of a control group of healthy subjects, matched for age and sex, by statistical parametric mapping (SPM) to identify areas of relative hypo- and hypermetabolism. Furthermore, the existence of a correlation between areas of hypo- and hypermetabolism in the patient group was tested.

RESULTS

Seven iNPH patients (mean age 74 ± 6 years) were found in the hospital database. SPM group analysis revealed clusters of significant hypometabolism ( P  = 0.001) in the iNPH group in the dorsal striatum, involving caudate and putamen bilaterally. Clusters of significant hypermetabolism ( P  = 0.001) were revealed in the bilateral superior and precentral frontal gyrus (BA 4, 6). A significant inverse correlation between striatal hypometabolism and bilateral superior and precentral frontal gyrus hypermetabolism was revealed ( P  < 0.001 corrected for multiple comparisons).

CONCLUSION

In this cohort, patients with iNPH showed subcortical hypometabolism, including bilateral dorsal striatum. To the best of our knowledge, this is the first report demonstrating a hypermetabolic pattern in the primary motor and premotor areas, and showing an inverse correlation between the striatum and motor cortex in patients with iNPH.

Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70-94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

Amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a fatal CNS neurodegenerative disease. Despite intensive research, current management of amyotrophic lateral sclerosis remains suboptimal from diagnosis to prognosis. Recognition of the phenotypic heterogeneity of amyotrophic lateral sclerosis, global CNS dysfunction, genetic architecture, and development of novel diagnostic criteria is clarifying the spectrum of clinical presentation and facilitating diagnosis. Insights into the pathophysiology of amyotrophic lateral sclerosis, identification of disease biomarkers and modifiable risks, along with new predictive models, scales, and scoring systems, and a clinical trial pipeline of mechanism-based therapies, are changing the prognostic landscape. Although most recent advances have yet to translate into patient benefit, the idea of amyotrophic lateral sclerosis as a complex syndrome is already having tangible effects in the clinic. This Seminar will outline these insights and discuss the status of the management of amyotrophic lateral sclerosis for the general neurologist, along with future prospects that could improve care and outcomes for patients with amyotrophic lateral sclerosis.

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.

Recent advances in the diagnosis and prognosis of amyotrophic lateral sclerosis

The diagnosis of amyotrophic lateral sclerosis can be challenging due to its heterogeneity in clinical presentation and overlap with other neurological disorders. Diagnosis early in the disease course can improve outcomes as timely interventions can slow disease progression. An evolving awareness of disease genotypes and phenotypes and new diagnostic criteria, such as the recent Gold Coast criteria, could expedite diagnosis. Improved prognosis, such as that achieved with the survival model from the European Network for the Cure of ALS, could inform the patient and their family about disease course and improve end-of-life planning. Novel staging and scoring systems can help monitor disease progression and might potentially serve as clinical trial outcomes. Lastly, new tools, such as fluid biomarkers, imaging modalities, and neuromuscular electrophysiological measurements, might increase diagnostic and prognostic accuracy.

Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review

Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.

Non-invasive characterization of amyotrophic lateral sclerosis in a hTDP-43A315T mouse model: A PET-MR study

Currently TAR DNA binding protein 43 (TDP-43) pathology, underlying Amyotrophic Lateral Sclerosis (ALS), remains poorly understood which hinders both clinical diagnosis and drug discovery efforts. To better comprehend the disease pathophysiology, positron emission tomography (PET) and multi-parametric magnetic resonance imaging (mp-MRI) provide a non-invasive mode to investigate molecular, structural, and neurochemical abnormalities in vivo. For the first time, we report the findings of a longitudinal PET-MR study in the TDP-43^A315T ALS mouse model, investigating disease-related changes in the mouse brain. 2-deoxy-2-[¹⁸F]fluoro-D-glucose [¹⁸F]FDG PET showed significantly lowered glucose metabolism in the motor and somatosensory cortices of TDP-43^A315T mice whereas metabolism was elevated in the region covering the bilateral substantia nigra, reticular and amygdaloid nucleus between 3 and 7 months of age, as compared to non-transgenic controls. MR spectroscopy data showed significant changes in glutamate + glutamine (Glx) and choline levels in the motor cortex and hindbrain of TDP-43^A315T mice compared to controls. Cerebral blood flow (CBF) measurements, using an arterial spin labelling approach, showed no significant age- or group-dependent changes in brain perfusion. Diffusion MRI indices demonstrated transient changes in different motor areas of the brain in TDP-43^A315T mice around 14 months of age. Cytoplasmic TDP-43 proteinaceous inclusions were observed in the brains of symptomatic, 18-month-old mice, but not in non-symptomatic transgenic or wild-type mice. Our results reveal that disease- and age-related functional and neurochemical alterations, together with limited structural changes, occur in specific brain regions of transgenic TDP-43^A315T mice, as compared to their healthy counterparts. Altogether these findings shed new light on TDP-43^A315T disease pathogenesis and may prove useful for clinical management of ALS.

Lifetime sport practice and brain metabolism in Amyotrophic Lateral Sclerosis

•

The possible impact of lifetime physical activity on the risk of ALS is debated.

•

Brain¹⁸F-FDG-PET is a marker of neuronal integrity in vivo.

•

We compared cases who did not practice sport (N), cases who did (Y) and controls.

•

N had more extensive changes in areas involved in ALS at the same disability level.

•

N might cope better with the neurodegenerative process compared to Y.

Objective

To evaluate the metabolic correlates of lifetime sport practice in ALS through brain ¹⁸F-FDG-PET.

Methods

131 patients completed a questionnaire about lifetime exposures, including physical activity related to sports, hobbies and occupations, and underwent brain ¹⁸F-FDG-PET. Exposure to sports was expressed as MET (Metabolic Equivalent of Task). We considered only regular practice (at least 2 h/week, for at least three months). We compared brain metabolism between two groups: subjects who did not report regular sport practice during life (N-group) and patients who did (Y-group). The resulting significant clusters were used in each group as seed regions in an interregional correlation analysis (IRCA) to evaluate the impact of lifetime sport practice on brain networks typically involved by the neurodegenerative process of ALS. Each group was compared to healthy controls (HC, n = 40).

Results

We found a significant, relative cerebellar hypermetabolism in the N-group compared to the Y-group. The metabolism of such cerebellar cluster resulted correlated to more significant and widespread metabolic changes in areas known to be affected by ALS (i.e. frontotemporal regions and corticospinal tracts) in the N-group as compared to the Y-group, despite the same level of disability as expressed by the ALS FRS-R. Such findings resulted independent of age, sex, site of onset (bulbar/spinal), presence/absence of C9ORF72 expansion, cognitive status and physical activity related to hobbies and occupations. When compared to HC, the N-group showed more widespread metabolic changes than the Y-group in cortical regions known to be relatively hypometabolic in ALS patients as compared to HC.

Conclusions

We hypothesize that patients of the N-group might cope better with the neurodegenerative process, since they show more widespread metabolic changes as compared to the Y-group, despite the same level of disability. Nevertheless, further studies are necessary to corroborate this hypothesis.

Are Circulating Cytokines Reliable Biomarkers for Amyotrophic Lateral Sclerosis?

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that has no effective treatment. The lack of any specific biomarker that can help in the diagnosis or prognosis of ALS has made the identification of biomarkers an urgent challenge. Multiple panels have shown alterations in levels of numerous cytokines in ALS, supporting the contribution of neuroinflammation to the progressive motor neuron loss. However, none of them is fully sensitive and specific enough to become a universal biomarker for ALS. This review gathers the numerous circulating cytokines that have been found dysregulated in both ALS animal models and patients. Particularly, it highlights the opposing results found in the literature to date, and points out another potential application of inflammatory cytokines as therapeutic targets.

Biomarkers in Motor Neuron Disease: A State of the Art Review

Motor neuron disease can be viewed as an umbrella term describing a heterogeneous group of conditions, all of which are relentlessly progressive and ultimately fatal. The average life expectancy is 2 years, but with a broad range of months to decades. Biomarker research deepens disease understanding through exploration of pathophysiological mechanisms which, in turn, highlights targets for novel therapies. It also allows differentiation of the disease population into sub-groups, which serves two general purposes: (a) provides clinicians with information to better guide their patients in terms of disease progression, and (b) guides clinical trial design so that an intervention may be shown to be effective if population variation is controlled for. Biomarkers also have the potential to provide monitoring during clinical trials to ensure target engagement. This review highlights biomarkers that have emerged from the fields of systemic measurements including biochemistry (blood, cerebrospinal fluid, and urine analysis); imaging and electrophysiology, and gives examples of how a combinatorial approach may yield the best results. We emphasize the importance of systematic sample collection and analysis, and the need to correlate biomarker findings with detailed phenotype and genotype data.

Positron Emission Tomography Molecular Imaging Biomarkers for Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with limited treatment options. Despite decades of therapeutic development, only two modestly efficacious disease-modifying drugs-riluzole and edaravone-are available to ALS patients. Biomarkers that can facilitate ALS diagnosis, aid in prognosis, and measure drug pharmacodynamics are needed to accelerate therapeutic development for patients with ALS. Positron emission tomography (PET) imaging has promise as a biomarker for ALS because it permits visualization of central nervous system (CNS) pathology in individuals living with ALS. The availability of PET radioligands that target a variety of potential pathophysiological mechanisms-including cerebral metabolism, neuroinflammation, neuronal dysfunction, and oxidative stress-has enabled dynamic interrogation of molecular changes in ALS, in both natural history studies and human clinical trials. PET imaging has potential as a diagnostic biomarker that can establish upper motor neuron (UMN) pathology in ALS patients without overt UMN symptoms, as a prognostic biomarker that might help stratify patients for clinical trials, and as a pharmacodynamic biomarker that measures the biological effect of investigational drugs in the brain and spinal cord. In this Review, we discuss progress made with 30 years of PET imaging studies in ALS and consider future research needed to establish PET imaging biomarkers for ALS therapeutic development.

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.