Phenotypic variability in mnd: clues to site of origin and patterns of disease spread

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Disease heterogeneity in motor neuron disease (MND) may suggest variable sites of disease onset, differences in prognosis and differing response to therapies. The clinical phenotype provides prognostic insight and information regarding the spread of disease. However, candidate biomarkers of disease have shown variable sensitivity and specificity. The present study used multimodal analysis to evaluate patterns of disease across MND phenotypes to assess the complex interplay of upper and lower motor neuronal dysfunction.


Newly diagnosed MND patients prospectively underwent clinical assessment and were classified according to their phenotype. Muscle and nerve ultrasound, neurophysiological studies and threshold tracking TMS (TT–TMS) were performed in all four limbs. Cognitive testing and diffusion tensor imaging (DTI, peripheral and central) were additionally performed. The differences in neuronal vulnerability within each phenotype were analysed cross-sectionally and longitudinally.


Techniques were first established in controls. Lower limb TT-TMS normal values (n=35) showed no statistically significant difference from upper limb (11.5+1.2 vs 11.3+1.1) and no significant interhemispheric differences (p=0.8). Novel testing of all 4-limbs in patients revealed differences in the pattern and spread of UMN and LMN vulnerability. Peripheral DTI metrics identified early axonal degeneration in the pre-symptomatic upper limb, correlating with clinical and neurophysiological markers of disease progression. Cortical hyperexcitability was associated with the clinically symptomatic limb as well site of onset in all phenotypes. In upper limb onset patients, clinical asymmetry correlated with synchronous asymmetry in LMN and UMN involvement, with significant interhemispheric differences in cortical hyperexcitability (p=0.05).


This phenotype specific analysis is allowing different patterns of neuronal vulnerability to emerge, with the potential to use these multimodal techniques to develop robust biomarkers of disease. This work is enabling a greater understanding of the patterns of disease spread, will identify prognostic differences across phenotypes and inevitably, response to new therapies.

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