Diffusion tensor imaging and quantitative susceptibility mapping as diagnostic tools for motor neuron disorders

Potential of neuroimaging as a biomarker in amyotrophic lateral sclerosis: from structure to metabolism

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron degeneration. The development of ALS involves metabolite alterations leading to tissue lesions in the nervous system. Recent advances in neuroimaging have significantly improved our understanding of the underlying pathophysiology of ALS, with findings supporting the corticoefferent axonal disease progression theory. Current studies on neuroimaging in ALS have demonstrated inconsistencies, which may be due to small sample sizes, insufficient statistical power, overinterpretation of findings, and the inherent heterogeneity of ALS. Deriving meaningful conclusions solely from individual imaging metrics in ALS studies remains challenging, and integrating multimodal imaging techniques shows promise for detecting valuable ALS biomarkers. In addition to giving an overview of the principles and techniques of different neuroimaging modalities, this review describes the potential of neuroimaging biomarkers in the diagnosis and prognostication of ALS. We provide an insight into the underlying pathology, highlighting the need for standardized protocols and multicenter collaborations to advance ALS research.

Iron quantification in basal ganglia using quantitative susceptibility mapping in a patient with ALS: a case report and literature review

Background

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can measure the magnetic susceptibility of tissues, which can reflect their iron content. QSM has been used to detect iron accumulation in cortical and subcortical brain regions. However, its application in subcortical regions such as the basal ganglia, particularly the putamen, is rare in patients with amyotrophic lateral sclerosis (ALS).

Case presentation and literature review

We present the case of a 40-year-old male patient with ALS who underwent an MRI for QSM. We compared his QSM images with those of a control subject and performed a quantitative analysis of the magnetic susceptibility values in the putamen regions. We also reviewed the literature on previous QSM studies in ALS and summarized their methods and findings. Our QSM analysis revealed increased magnetic susceptibility values in the bilateral putamen of the ALS patient compared to controls, indicating iron overload. This finding is consistent with previous studies reporting iron dysregulation in subcortical nuclei in ALS. We also discussed the QSM processing techniques used in our study and in the literature, highlighting their advantages and limitations.

Conclusion

This case report demonstrates the potential of QSM as a sensitive MRI biomarker for evaluating iron levels in subcortical regions of ALS patients. QSM can provide quantitative information on iron deposition patterns in both motor and extra-motor areas of ALS patients, which may help understand the pathophysiology of ALS and monitor disease progression. Further studies with larger samples are needed to validate these results and explore the clinical implications of QSM in ALS.

Prognostic Usefulness of Motor Unit Number Index (MUNIX) in Patients Newly Diagnosed with Amyotrophic Lateral Sclerosis

The MUNIX technique allows us to estimate the number and size of surviving motor units (MUs). Previous studies on ALS found correlations between MUNIX and several clinical measures, but its potential role as a predictor of disease progression rate (DPR) has not been thoroughly evaluated to date. We aimed to investigate MUNIX's ability to predict DPR at a six-month follow up.

METHODS

24 ALS patients with short disease duration (<24 months from symptoms' onset) were enrolled and divided according to their baseline DPR into two groups (normal [DPR-N] and fast [DPR-F] progressors). MUNIX values were obtained from five muscles (TA, APB, ADM, FDI, Trapezius) and averaged for each subject.

RESULTS

MUNIX was found to predict DPR at follow up in a multivariable linear regression model; namely, patients with lower MUNIX values were at risk of showing greater DPR scores at follow up. The result was replicated in a simple logistic regression analysis, with the dichotomic category "MUNIX-Low" as the independent variable and the outcome "DPR-F" as the dependent variable.

CONCLUSIONS

our results pave the way for the use of the MUNIX method as a prognostic tool in early ALS, enabling patients' stratification according to their rates of future decline.

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.

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

Background:

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

Methods:

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

Results:

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

Conclusions:

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

Combining structural and metabolic markers in a quantitative MRI study of motor neuron diseases

OBJECTIVE

To assess the performance of a combination of three quantitative MRI markers (iron deposition, basal neuronal metabolism, and regional atrophy) for differential diagnosis between amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS).

METHODS

In total, 33 ALS, 12 PLS, and 28 healthy control (HC) subjects underwent a 3T MRI study including single- and multi-echo sequences for gray matter (GM) volumetry and quantitative susceptibility mapping (QSM) and a pseudo-continuous arterial spin labeling (ASL) sequence for cerebral blood flow (CBF) measurement. Mean values of QSM, CBF, and GM volumes were extracted in the motor cortex, basal ganglia, thalamus, amygdala, and hippocampus. A generalized linear model was applied to the three measures to binary discriminate between groups. The diagnostic performances were evaluated via receiver operating characteristic analyses.

RESULTS

A significant discrimination was obtained: between ALS and HCs in the left and right motor cortex, where QSM increases were respectively associated with disability scores and disease duration; between PLS and ALS in the left motor cortex, where PLS patients resulted significantly more atrophic; between ALS and HC in the right motor cortex, where GM volumes were associated with upper motor neuron scores. Significant discrimination between ALS and HC was achieved in subcortical structures only combining all three parameters.

INTERPRETATION

While increased QSM values in the motor cortex of ALS patients is a consolidated finding, combining QSM, CBF, and GM volumetry shows higher diagnostic potential for differentiating ALS patients from HC subjects and, in the motor cortex, between ALS and PLS.

Systematic Review: Quantitative Susceptibility Mapping (QSM) of Brain Iron Profile in Neurodegenerative Diseases

Iron has been increasingly implicated in the pathology of neurodegenerative diseases. In the past decade, development of the new magnetic resonance imaging technique, quantitative susceptibility mapping (QSM), has enabled for the more comprehensive investigation of iron distribution in the brain. The aim of this systematic review was to provide a synthesis of the findings from existing QSM studies in neurodegenerative diseases. We identified 80 records by searching MEDLINE, Embase, Scopus, and PsycInfo databases. The disorders investigated in these studies included Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Wilson's disease, Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia, Fabry disease, myotonic dystrophy, pantothenate-kinase-associated neurodegeneration, and mitochondrial membrane protein-associated neurodegeneration. As a general pattern, QSM revealed increased magnetic susceptibility (suggestive of increased iron content) in the brain regions associated with the pathology of each disorder, such as the amygdala and caudate nucleus in Alzheimer's disease, the substantia nigra in Parkinson's disease, motor cortex in amyotrophic lateral sclerosis, basal ganglia in Huntington's disease, and cerebellar dentate nucleus in Friedreich's ataxia. Furthermore, the increased magnetic susceptibility correlated with disease duration and severity of clinical features in some disorders. Although the number of studies is still limited in most of the neurodegenerative diseases, the existing evidence suggests that QSM can be a promising tool in the investigation of neurodegeneration.

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.

A specific biomarker for amyotrophic lateral sclerosis: Quantitative susceptibility mapping

OBJECTIVE

Accurate and timely diagnosis of amyotrophic lateral sclerosis (ALS) is a diagnostic challenge given the lack of specific diagnostic and imaging biomarkers as well as the significant clinic overlap with mimic syndromes. We hypothesize that MR quantitative susceptibility mapping (QSM) can help differentiate ALS from mimic diagnoses.

METHODS

In a blinded retrospective study of MRIs with QSM from 2015 to 2018, we compared motor cortex susceptibility along the hand and face homunculi in ALS patients and patients with similar clinical presentations. Inclusion required a confirmed ALS or a mimic diagnosis. Comparative groups included age-matched patients with MRIs performed for non-motor neuron symptoms that were reported as normal or demonstrated leukoaraiosis. Quantitative susceptibility values were compared with ANOVA and Tukey-Kramer (post-hoc). ROC analysis and Youden's index were used to identify optimal cutoff values.

RESULTS

Fifty ALS, 35 mimic, and 70 non-motor neuron symptom patients (35 normal, 35 leukoaraiosis) were included. Hand and face homunculus mean susceptibility values were significantly higher in the ALS group compared to the mimic (p=0.001, p=0.004), leukoaraiosis (p<0.001, p=0.003), and normal (p<0.001, p<0.001) groups. ROC curve analysis comparing ALS to mimics resulted in an area under the curve of 0.71 and 0.67 for the hand and face homunculus measurements, respectively. In differentiating ALS from mimics, Youden's index showed 100% specificity and 36% sensitivity for hand homunculus measurements.

CONCLUSIONS

QSM has diagnostic potential in the assessment of suspected ALS patients, demonstrating very high specificity in differentiating ALS from mimic diagnoses.

Serial assessment of iron in the motor cortex in limb-onset amyotrophic lateral sclerosis using quantitative susceptibility mapping

BACKGROUND

Dysregulation of iron in the cerebral motor areas has been hypothesized to occur in individuals with amyotrophic lateral sclerosis (ALS). There is still limited knowledge regarding iron dysregulation in the progression of ALS pathology. Our objectives were to use magnetic resonance based quantitative susceptibility mapping (QSM) to investigate the association between iron dysregulation in the motor cortex and clinical manifestations in patients with limb-onset ALS, and to examine changes in the iron concentration in the motor cortex in these patients over a 6-month period.

METHODS

Iron concentration was investigated using magnetic resonance based QSM in the primary motor cortex and the pre-motor area in 13 limb-onset ALS patients (including five lumbar onset, six cervical onset and two flail arm patients), and 11 age- and sex-matched control subjects. Nine ALS patients underwent follow-up scans at 6 months.

RESULTS

Significantly increased QSM values were observed in the left posterior primary motor area (P=0.02, Cohen's d =0.9) and right anterior primary motor area (P=0.02, Cohen's d =0.92) in the group of limb-onset ALS patients compared to that of control subjects. Increased QSM was observed in the primary motor and pre-motor area at baseline in patients with lumbar onset ALS patients, but not cervical limb-onset ALS patients, compared to control subjects. No significant change in QSM was observed at the 6-month follow-up scans in the ALS patients.

CONCLUSIONS

The findings suggest that iron dysregulation can be detected in the motor cortex in limb-onset ALS, which does not appreciably change over a further 6 months. Individuals with lumbar onset ALS appear to be more susceptible to motor cortex iron dysregulation compared to the individuals with cervical onset ALS. Importantly, this study highlights the potential use of QSM as a quantitative radiological indicator in early disease diagnosis in limb-onset ALS and its subtypes. Our serial scans results suggest a longer period than 6 months is needed to detect significant quantitative changes in the motor cortex.

Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis

Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls.

Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism

BACKGROUND

Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice.

OBJECTIVE

To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community.

METHOD

We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility.

RESULTS

We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase.

CONCLUSION

Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.