An Automated Tongue Tracker for Quantifying Bulbar Function in ALS

Multimodal layer modelling reveals in vivo pathology in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease characterized by the loss of motor control. Current understanding of ALS pathology is largely based on post-mortem investigations at advanced disease stages. A systematic in vivo description of the microstructural changes that characterize early stage ALS, and their subsequent development, is so far lacking. Recent advances in ultra-high field (7 T) MRI data modelling allow us to investigate cortical layers in vivo. Given the layer-specific and topographic signature of ALS pathology, we combined submillimetre structural 7 T MRI data (qT1, QSM), functional localizers of body parts (upper limb, lower limb, face) and layer modelling to systematically describe pathology in the primary motor cortex (M1), in 12 living ALS patients with reference to 12 matched controls. Longitudinal sampling was performed for a subset of patients. We calculated multimodal pathology maps for each layer (superficial layer, layer 5a, layer 5b, layer 6) of M1 to identify hot spots of demyelination, iron and calcium accumulation in different cortical fields. We show preserved mean cortical thickness and layer architecture of M1, despite significantly increased iron in layer 6 and significantly increased calcium in layer 5a and superficial layer, in patients compared to controls. The behaviourally first-affected cortical field shows significantly increased iron in L6 compared to other fields, while calcium accumulation is atopographic and significantly increased in the low myelin borders between cortical fields compared to the fields themselves. A subset of patients with longitudinal data shows that the low myelin borders are particularly disrupted and that calcium hot spots, but to a lesser extent iron hot spots, precede demyelination. Finally, we highlight that a very slow progressing patient (Patient P4) shows a distinct pathology profile compared to the other patients. Our data show that layer-specific markers of in vivo pathology can be identified in ALS patients with a single 7 T MRI measurement after first diagnosis, and that such data provide critical insights into the individual disease state. Our data highlight the non-topographic architecture of ALS disease spread and the role of calcium, rather than iron accumulation, in predicting future demyelination. We also highlight a potentially important role of low myelin borders, that are known to connect to multiple areas within the M1 architecture, in disease spread. Finally, the distinct pathology profile of a very-slow progressing patient (Patient P4) highlights a distinction between disease duration and progression. Our findings demonstrate the importance of in vivo histology imaging for the diagnosis and prognosis of neurodegenerative diseases such as ALS.

Clinical Neurology in Practice: The Tongue (Part 1)

BACKGROUND

The tongue is an essential organ for the development of certain crucial functions, such as swallowing and language. The examination of the tongue can be very useful in neurology, as the various types of lingual alterations can lead to certain specific diagnoses, the tongue being a kind of "mirror" of some neurological function.

REVIEW SUMMARY

In this study, we reviewed the literature on anatomy, physiology, and the various aspects of the examination of the tongue.

CONCLUSIONS

Examination of the tongue should be an integral part of the clinical examination of the cranial nerves. This study aimed to demonstrate the importance of tongue motor and non-motor functions in neurological practice.

Ultrasonographic and manometric study of the tongue as biomarkers of dysphagia in patients with amyotrophic lateral sclerosis

BACKGROUND

The possibility of having methods to assess dysphagia in amyotrophic lateral sclerosis (ALS) patients in a minimally invasive manner could facilitate follow-up and allow performing of therapeutic interventions at earlier stages of the disease. The aim of the study was to analyze the role of tongue strength and thickness in ALS patients and their correlation with dysphagia and bulbar function.

METHODS

A sample of outpatients with ALS was evaluated for demographic and clinical features. Tongue thickness and strength have been measured for each patient, and quantitative and qualitative data of the videofluoroscopy swallow study have been analyzed.

RESULTS

Of the 38 ALS patients studied, 47.4% were women, and 26.3% had bulbar onset. The median time between symptom onset and the study was 24 months (IQR 11.5-48), and 55.3% of the patients were carriers of non-invasive mechanical ventilation. Tongue strength identified patients with impaired oral and pharyngeal transit and those with bolus residue scale (BRS) > 1 or penetration-aspiration scale (PAS) ≥ 3. In contrast, tongue thickness is only associated with impaired oral transit. Finally, anterior tongue strength ≤ 34 kPa and posterior tongue strength ≤ 34.5 kPa detected ALS penetrators/aspirators (PAS ≥ 3) and patients with ALS with post-swallow residue (BRS > 1).

CONCLUSIONS

Our results suggest that measures that assess the functionality (strength) of the tongue are more valuable than morphological measurements (thickness) for the follow-up of patients with ALS. Alterations of the anterior and posterior lingual strength correlate with the presence of bronchoaspiration and post-swallowing residue (BRS > 1).

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms

Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.