Regional callosal integrity and bilaterality of limb weakness in amyotrophic lateral sclerosis

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

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

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.

Preliminary evidence of links between ayahuasca use and the corpus callosum

Background

Recent research suggests that ayahuasca and its alkaloid-containing ingredients may be helpful in the treatment and prevention of certain movement and neurodegenerative disorders. However, such research is still in its infancy and more studies in normative samples seem necessary to explore effects of ayahuasca on clinically relevant brain structures, such as the corpus callosum.

Aims

The purpose of the present study was to investigate links between ayahuasca use and callosal structure in a normative sample.

Methods

Using structural imaging data from 22 ayahuasca users and 22 matched controls we compared the thickness of the corpus callosum between both groups at 100 equidistant points across the entire midsagittal surface. In addition, we investigated point-wise correlations between callosal thickness and the number of past ayahuasca sessions.

Results

The corpus callosum was significantly thicker within the isthmus in the ayahuasca group than in the control group. There was also a significant positive correlation between callosal thickness and the number of past ayahuasca sessions within the rostral body, albeit none of these effects survived corrections for multiple comparisons. No region was significantly thicker in the control than in the ayahuasca group, and no callosal region was negatively linked to ayahuasca use, even at uncorrected significance thresholds.

Conclusion

This study provides preliminary evidence of links between ayahuasca use and the corpus callosum. However, future studies need to replicate these findings, preferably using larger sample sizes and ideally also utilizing longitudinal research designs, to draw any practical conclusion and offer implications for follow-up clinical research.

Neuropathology and neuroanatomy of TDP-43 amyotrophic lateral sclerosis

PURPOSE OF REVIEW

Intracellular inclusions consisting of the abnormal TDP-43 protein and its nucleocytoplasmic mislocalization in selected cell types are hallmark pathological features of sALS. Descriptive (histological, morphological), anatomical, and molecular studies all have improved our understanding of the neuropathology of sporadic amyotrophic lateral sclerosis (sALS). This review highlights some of the latest developments in the field.

RECENT FINDINGS

Increasing evidence exists from experimental models for the prion-like nature of abnormal TDP-43, including a strain-effect, and with the help of neuroimaging-based studies, for spreading of disease along corticofugal connectivities in sALS. Progress has also been made with respect to finding and establishing reliable biomarkers (neurofilament levels, diffusor tensor imaging).

SUMMARY

The latest findings may help to elucidate the preclinical phase of sALS and to define possible mechanisms for delaying or halting disease development and progression.

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.

Brain Connectivity and Network Analysis in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with no effective treatment or cure. ALS is characterized by the death of lower motor neurons (LMNs) in the spinal cord and upper motor neurons (UMNs) in the brain and their networks. Since the lower motor neurons are under the control of UMN and the networks, cortical degeneration may play a vital role in the pathophysiology of ALS. These changes that are not apparent on routine imaging with CT scans or MRI brain can be identified using modalities such as diffusion tensor imaging, functional MRI, arterial spin labelling (ASL), electroencephalogram (EEG), magnetoencephalogram (MEG), functional near-infrared spectroscopy (fNIRS), and positron emission tomography (PET) scan. They can help us generate a representation of brain networks and connectivity that can be visualized and parsed out to characterize and quantify the underlying pathophysiology in ALS. In addition, network analysis using graph measures provides a novel way of understanding the complex network changes occurring in the brain. These have the potential to become biomarker for the diagnosis and treatment of ALS. This article is a systematic review and overview of the various connectivity and network-based studies in ALS.

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.

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.

Segmental involvement of the corpus callosum in C9orf72-associated ALS: a tract of interest-based DTI study

Background:

C9orf72 hexanucleotide repeat expansions are associated with widespread cerebral alterations, including white matter alterations. However, there is lack of information on changes in commissure fibres. Diffusion tensor imaging (DTI) can identify amyotrophic lateral sclerosis (ALS)-associated patterns of regional brain alterations at the group level. The objective of this study was to investigate the structural connectivity of the corpus callosum (CC) in ALS patients with C9orf72 expansions.

Methods:

DTI-based white matter mapping was performed by a hypothesis-guided tractwise analysis of fractional anisotropy (FA) maps for 25 ALS patients with C9orf72 expansion versus 25 matched healthy controls. Furthermore, a comparison with a patient control group of 25 sporadic ALS patients was performed. DTI-based tracts that originate from callosal sub-areas I to V were identified and correlated with clinical data.

Results:

The analysis of white matter integrity demonstrated regional FA reductions for tracts of the callosal areas II and III for ALS patients with C9orf72 expansions while FA reductions in sporadic ALS patients were observed only for tracts of the callosal area III; these reductions were correlated with clinical parameters.

Conclusion:

The tract-of-interest-based analysis showed a microstructural callosal involvement pattern in C9orf72-associated ALS that included the motor segment III together with frontal callosal connections, as an imaging signature of the C9orf72-associated overlap of motor neuron disease and frontotemporal pathology.

The Dying Forward Hypothesis of ALS: Tracing Its History

The site of origin of amyotrophic lateral sclerosis (ALS), although unsettled, is increasingly recognized as being cortico-fugal, which is a dying-forward process primarily starting in the corticomotoneuronal system. A variety of iterations of this concept date back to over 150 years. Recently, the hallmark TAR DNA-binding protein 43 (TDP-43) pathology, seen in >95% of patients with ALS, has been shown to be largely restricted to corticofugal projecting neurons (“dying forward”). Possibly, soluble but toxic cytoplasmic TDP-43 could enter the axoplasm of Betz cells, subsequently causing dysregulation of nuclear protein in the lower brainstem and spinal cord anterior horn cells. As the disease progresses, cortical involvement in ALS becomes widespread, including or starting with frontotemporal dementia, implying a broader view of ALS as a brain disease. The onset at the motor and premotor cortices should be considered a nidus at the edge of multiple cortical networks which eventually become disrupted, causing failure of a widespread cortical connectome.

Pathophysiological associations of transcallosal dysfunction in ALS

AIM

Involvement of the corpus callosum has been identified as a feature of amyotrophic lateral sclerosis (ALS), particularly through neuropathological studies. The aim of the present study was to determine whether alteration in transcallosal function contributed to the development of ALS, disease progression and thereby functional disability.

METHODS

Transcallosal function and motor cortex excitability were assessed in 17 ALS patients with results compared to healthy controls. Transcallosal inhibition (interstimulus intervals (ISI) of 8-40 ms), short interval intracortical facilitation (SICF) and inhibition (SICI) were assessed in both cerebral hemispheres. Patients were staged utilising clinical and neurophysiological staging assessments.

RESULTS

In ALS, there was prominent reduction of transcallosal inhibition (TI) when recorded from the primary and secondary motor cortices compared to controls (F = 23.255, p < 0.001). This reduction of TI was accompanied by features indicative of cortical hyperexcitability, including reduction of SICI and increase in SICF. There was a significant correlation between the reduction in TI and the rate of disease progression (R = -0.825, p < 0.001) and reduction in muscle strength (R = 0.54, p = 0.031).

CONCLUSION

The present study has established that dysfunction of transcallosal circuits was an important pathophysiological mechanism in ALS, correlating with greater disability and a faster rate of disease progression. Therapies aimed at restoring the function of transcallosal circuits may be considered for therapeutic approaches in ALS.

Air Pollution-Related Brain Metal Dyshomeostasis as a Potential Risk Factor for Neurodevelopmental Disorders and Neurodegenerative Diseases

Increasing evidence links air pollution (AP) exposure to effects on the central nervous system structure and function. Particulate matter AP, especially the ultrafine (nanoparticle) components, can carry numerous metal and trace element contaminants that can reach the brain in utero and after birth. Excess brain exposure to either essential or non-essential elements can result in brain dyshomeostasis, which has been implicated in both neurodevelopmental disorders (NDDs; autism spectrum disorder, schizophrenia, and attention deficit hyperactivity disorder) and neurodegenerative diseases (NDGDs; Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis). This review summarizes the current understanding of the extent to which the inhalational or intranasal instillation of metals reproduces in vivo the shared features of NDDs and NDGDs, including enlarged lateral ventricles, alterations in myelination, glutamatergic dysfunction, neuronal cell death, inflammation, microglial activation, oxidative stress, mitochondrial dysfunction, altered social behaviors, cognitive dysfunction, and impulsivity. Although evidence is limited to date, neuronal cell death, oxidative stress, and mitochondrial dysfunction are reproduced by numerous metals. Understanding the specific contribution of metals/trace elements to this neurotoxicity can guide the development of more realistic animal exposure models of human AP exposure and consequently lead to a more meaningful approach to mechanistic studies, potential intervention strategies, and regulatory requirements.