Graph theory network analysis provides brain MRI evidence of a partial continuum of neurodegeneration in patients with UMN-predominant ALS and ALS-FTD

New developments in imaging in ALS

Neuroimaging in ALS has contributed considerable academic insights in recent years demonstrating genotype-specific topological changes decades before phenoconversion and characterising longitudinal propagation patterns in specific phenotypes. It has elucidated the radiological underpinnings of specific clinical phenomena such as pseudobulbar affect, apathy, behavioural change, spasticity, and language deficits. Academic concepts such as sexual dimorphism, motor reserve, cognitive reserve, adaptive changes, connectivity-based propagation, pathological stages, and compensatory mechanisms have also been evaluated by imaging. The underpinnings of extra-motor manifestations such as cerebellar, sensory, extrapyramidal and cognitive symptoms have been studied by purpose-designed imaging protocols. Clustering approaches have been implemented to uncover radiologically distinct disease subtypes and machine-learning models have been piloted to accurately classify individual patients into relevant diagnostic, phenotypic, and prognostic categories. Prediction models have been developed for survival in symptomatic patients and phenoconversion in asymptomatic mutation carriers. A range of novel imaging modalities have been implemented and 7 Tesla MRI platforms are increasingly being used in ALS studies. Non-ALS MND conditions, such as PLS, SBMA, and SMA, are now also being increasingly studied by quantitative neuroimaging approaches. A unifying theme of recent imaging papers is the departure from describing focal brain changes to focusing on dynamic structural and functional connectivity alterations. Progressive cortico-cortical, cortico-basal, cortico-cerebellar, cortico-bulbar, and cortico-spinal disconnection has been consistently demonstrated by recent studies and recognised as the primary driver of clinical decline. These studies have led the reconceptualisation of ALS as a "network" or "circuitry disease".

Mapping motor and extra-motor gray and white matter changes in ALS: a comprehensive review of MRI insights

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor neurons, yet with substantial clinical variability. Furthermore, beyond motor symptoms, ALS patients also show non-motor features, reflecting its classification as a multi-system disorder. The identification of reliable biomarkers is a critical challenge for improving diagnosis, tracking disease progression, and predicting patient outcomes. This review explores macro- and microstructural alterations in ALS, focusing on gray matter (GM) and white matter (WM) as observed through Magnetic Resonance Imaging (MRI). This approach synthesizes not only the expected involvement of motor areas but also highlights emerging evidence that these changes extend to extra-motor areas, such as the frontal and temporal lobes, underscoring the complex pathophysiology of ALS. The review emphasizes the potential of MRI as a non-invasive tool to provide new biomarkers by assessing both GM and WM integrity, a key advancement in ALS research. Additionally, it addresses existing discrepancies in findings and stresses the need for standardized imaging protocols. It also highlights the role of multi-modal MRI approaches in deepening our understanding of ALS pathology, emphasizing the importance of combining structural and diffusion MRI techniques to offer more comprehensive insights into ALS progression, ultimately advancing the potential for personalized treatment strategies and improving patient outcomes.

White matter integrity mediated the effect of plasma uric acid levels on cognitive function in ALS patients

OBJECTIVE

To investigate the association between plasma uric acid levels and white matter microstructural alterations in amyotrophic lateral sclerosis (ALS) patients and to explore the potential mediating role of white matter microstructural alterations in the protective effect of plasma uric acid on cognitive function in ALS patients.

METHODS

73 right-handed ALS patients were recruited for this study. Plasma uric acid levels were measured, diffusion tensor imaging scans were performed to assess white matter integrity, and cognition was evaluated using the Edinburgh Cognitive and Behavioral Screen. The relationships among plasma uric acid, white matter integrity, and cognitive function were examined through multivariate linear regression analysis. Additionally, mediation analysis was performed to investigate whether white matter integrity mediated the relationship between uric acid levels and cognitive function.

RESULTS

The findings revealed a positive correlation between plasma uric acid levels and extensive preservation of white matter microstructure in various regions, including the fornix, cerebellar, internal capsule, frontotemporal and frontooccipital lobe bundles among ALS patients. Mediation analysis indicated that fractional anisotropy in the hippocampal portion of the cingulum fully mediated the effects of plasma uric acid levels on executive function in ALS patients.

INTERPRETATION

Our results suggested that elevated plasma uric acid may preserve the integrity of white matter microstructure in ALS patients. Furthermore, we have identified evidence supporting the mediating influence of the hippocampal portion of the cingulum in linking plasma uric acid levels to cognitive function among ALS patients.

Graph theory network analysis reveals widespread white matter damage in brains of patients with classic ALS

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) exhibits several different presentations and clinical phenotypes. Of these, classic ALS (ALS-Cl), which is the most common phenotype, presents with relatively equal amounts of upper motor neuron and lower motor neuron signs. Magnetic resonance imaging (MRI) provides a noninvasive way to assess central nervous system damage in these patients. To our knowledge no study is available where exploratory whole brain grey matter (GM) and white matter (WM) network analysis is performed considering only the ALS-Cl subgroup of ALS patients.

METHODS

GM voxel-based morphometry analysis and WM network analysis using graph theory was performed in the MRI dataset of 14 neurologic controls and 25 ALS-Cl patients.

RESULTS AND CONCLUSIONS

No significant GM differences were observed between ALS-Cl and neurologic controls. WM network revealed significant (p < 0.05) reduction and increase in degree measure in several extramotor brain regions of ALS-Cl patients. Both global and local graph metrics revealed significant abnormal values in ALS-Cl patients when compared to neurologic controls. Significant WM changes in ALS-Cl patients with no significant GM changes suggest that neurodegeneration may onset as an "axonopathy" in this ALS subtype.

Different baseline functional patterns of the frontal cortex in amyotrophic lateral sclerosis patients with Corticospinal tract hyperintensity

Nearly half of the amyotrophic lateral sclerosis (ALS) patients showed hyperintensity of the corticospinal tract (CST+), yet whether brain functional pattern differs between CST+and CST- patients remains obscure. In the current study, 19 ALS CST+, 41 ALS CST- patients and 37 healthy controls (HC) underwent resting state fMRI scans. We estimated local activity and connectivity patterns via the Amplitude of Low Frequency Fluctuations (ALFF) and the Network-Based Statistic (NBS) approaches respectively. The ALS CST+patients did not differ from the CST- patients in amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) score and disease duration. ALFF of the superior frontal gyrus (SFG) and the inferior frontal gyrus pars opercularis (OIFG) were highest in the HC and lowest in the ALS CST- patients, resulting in significant group differences (PFWE<0.05). NBS analysis revealed a frontal network consisting of connections between SFG, OIFG, orbital frontal gyrus, middle cingulate cortex and the basal ganglia, which exhibited HC>ALS CST+ > ALS CST- group differences (PFWE=0.037) as well. The ALFF of the OIFG was significantly correlated with ALSFRS-R (R=0.34, P=0.028) and mean connectivity of the frontal network was trend-wise significantly correlated with disease duration (R=-0.31, P=0.052) in the ALS CST- patients. However, these correlations were insignificant in ALS CST+patients (P values > 0.8). In conclusion, The ALS CST+patients exhibited different patterns of baseline functional activity and connectivity in the frontal cortex which may indicate a functional compensatory effect.

Differing patterns of cortical grey matter pathology identified by multifractal analysis in UMN-predominant ALS patients with and without corticospinal tract hyperintensity

The pathological hallmarks of amyotrophic lateral sclerosis (ALS) are degeneration of the primary motor cortex grey matter (GM) and corticospinal tract (CST) resulting in upper motor neuron (UMN) dysfunction. Conventional brain magnetic resonance imaging (MRI) shows abnormal CST hyperintensity in some UMN-predominant ALS patients (ALS-CST+) but not in others (ALS-CST-). In addition to the CST differences, we aimed to determine whether GM degeneration differs between ALS-CST+ and ALS-CST- patients by cortical thickness (CT), voxel-based morphometry (VBM) and fractal dimension analyses. We hypothesized that MRI multifractal (MF) measures could differentiate between neurologic controls (n = 14) and UMN-predominant ALS patients as well as between patient subgroups (ALS-CST+, n = 21 vs ALS-CST-, n = 27). No significant differences were observed in CT or GM VBM in any brain regions between patients and controls or between ALS subgroups. MF analyses were performed separately on GM of the whole brain, of frontal, parietal, occipital, and temporal lobes as well as of cerebellum. Estimating MF measures D (Q = 0), D (Q = 1), D (Q = 2), Δf, Δα of frontal lobe GM classified neurologic controls, ALS-CST+ and ALS-CST- groups with 98% accuracy and > 95% in F1, recall, precision and specificity scores. Classification accuracy was only 74% when using whole brain MF measures and < 70% for other brain lobes. We demonstrate that MF analysis can distinguish UMN-predominant ALS subgroups based on GM changes, which the more commonly used quantitative approaches of CT and VBM cannot.

MRI biomarkers and neuropsychological assessments of hippocampal and parahippocampal regions affected by ALS: A systematic review

BACKGROUND AND OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra-motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and neuropsychological assessments of the hippocampal and parahippocampal regions in patients with ALS.

METHODS

A systematic review was conducted in the Scopus and PubMed databases for studies published between January 2000 and July 2023. The inclusion criteria were (1) MRI studies to assess hippocampal and parahippocampal regions in ALS patients, and (2) studies reporting neuropsychological data in patients with ALS.

RESULTS

A total of 46 studies were included. Structural MRI revealed hippocampal atrophy, especially in ALS-FTD, involving specific subregions (CA1, dentate gyrus). Disease progression and genetic factors impacted atrophy patterns. Diffusion tensor imaging (DTI) showed increased mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in the hippocampal tracts and adjacent regions, indicating loss of neuronal and white matter integrity. Functional MRI (fMRI) revealed reduced functional connectivity (FC) between the hippocampus, parahippocampus, and other regions, suggesting disrupted networks. Perfusion MRI showed hypoperfusion in parahippocampal gyri. Magnetic resonance spectroscopy (MRS) found changes in the hippocampus, indicating neuronal loss. Neuropsychological tests showed associations between poorer memory and hippocampal atrophy or connectivity changes. CA1-2, dentate gyrus, and fimbria atrophy were correlated with worse memory.

CONCLUSIONS

The hippocampus and the connected regions are involved in ALS. Hippocampal atrophy disrupted connectivity and metabolite changes correlate with cognitive and functional decline. Specific subregions can be particularly affected. The hippocampus is a potential biomarker for disease monitoring and prognosis.

Biomarkers in amyotrophic lateral sclerosis: current status and future prospects

Disease heterogeneity in amyotrophic lateral sclerosis poses a substantial challenge in drug development. Categorization based on clinical features alone can help us predict the disease course and survival, but quantitative measures are also needed that can enhance the sensitivity of the clinical categorization. In this Review, we describe the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers in amyotrophic lateral sclerosis and their place in the rapidly evolving landscape of new therapeutics. Fluid-based markers from cerebrospinal fluid, blood and urine are emerging as useful diagnostic, pharmacodynamic and predictive biomarkers. Combinations of imaging measures have the potential to provide important diagnostic and prognostic information, and neurophysiological methods, including various electromyography-based measures and quantitative EEG-magnetoencephalography-evoked responses and corticomuscular coherence, are generating useful diagnostic, categorical and prognostic markers. Although none of these biomarker technologies has been fully incorporated into clinical practice or clinical trials as a primary outcome measure, strong evidence is accumulating to support their clinical utility.

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.

Hypometabolic and hypermetabolic brain regions in patients with ALS-FTD show distinct patterns of grey and white matter degeneration: A pilot multimodal neuroimaging study

BACKGROUND

Up to 50% of amyotrophic lateral sclerosis (ALS) patients develop some degree of cognitive dysfunction and a small proportion of these develop frontotemporal dementia (FTD). Non-invasive techniques of magnetic resonance imaging (MRI) and [¹⁸F]-fluoro-2-deoxy-d-glucose (¹⁸F-FDG) positron emission tomography (PET) have demonstrated structural and metabolic abnormalities, respectively, in the brains of such patients with ALS-FTD. Although initial ¹⁸F-FDG PET studies in ALS patients showed only hypometabolism of motor and extramotor brain regions, subsequent studies have demonstrated hypermetabolic changes as well. Such contrasting findings prompted us to hypothesize that hypo- and hypermetabolic brain regions in ALS-FTD patients are associated with divergent degeneration of structural grey matter (GM) and white matter (WM).

METHODS

Cerebral glucose metabolic rate (CMR_glc), cortical thickness (CT), fractal dimension (FD), and graph theory WM network analyses were performed on clinical MRI and ¹⁸F-FDG PET images from 8 ALS-FTD patients and 14 neurologic controls to explore the relationship between GM-WM degeneration and hypo- and hypermetabolic brain regions.

RESULTS

CMR_glc revealed significant hypometabolism in frontal and precentral gyrus brain regions, with hypermetabolism in temporal, occipital and cerebellar regions. Cortical thinning was noted in both hypo- and hypermetabolic brain areas. Unlike CT, FD did not reveal widespread GM degeneration in hypo- and hypermetabolic brain regions of ALS-FTD patients. Graph theory analysis showed severe WM degeneration in hypometabolic but not hypermetabolic areas, especially in the right hemisphere.

CONCLUSION

Our multimodal MRI-PET study provides insights into potentially differential pathophysiological mechanisms between hypo- and hypermetabolic brain regions of ALS-FTD patients.