Brain : a journal of neurology
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Diagnosis, stratification and monitoring of disease progression in amyotrophic lateral sclerosis currently rely on clinical history and examination. The phenotypic heterogeneity of amyotrophic lateral sclerosis, including extramotor cognitive impairments is now well recognized. Candidate biomarkers have shown variable sensitivity and specificity, and studies have been mainly undertaken only cross-sectionally. ⋯ It is also suggested that the upper motor neuron lesion in amyotrophic lateral sclerosis may be relatively constant during the established symptomatic period. These findings have implications for the development of effective diagnostic versus therapeutic monitoring magnetic resonance imaging biomarkers. Amyotrophic lateral sclerosis may be characterized initially by a predominantly white matter tract pathological signature, evolving as a widespread cortical network degeneration.
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Case Reports
Agrin mutations lead to a congenital myasthenic syndrome with distal muscle weakness and atrophy.
Congenital myasthenic syndromes are a clinically and genetically heterogeneous group of rare diseases resulting from impaired neuromuscular transmission. Their clinical hallmark is fatigable muscle weakness associated with a decremental muscle response to repetitive nerve stimulation and frequently related to postsynaptic defects. Distal myopathies form another clinically and genetically heterogeneous group of primary muscle disorders where weakness and atrophy are restricted to distal muscles, at least initially. ⋯ In our patients, we found two missense mutations residing in the N-terminal agrin domain, which reduced acetylcholine receptors clustering activity of agrin in vitro. Our findings expand the spectrum of congenital myasthenic syndromes due to agrin mutations and show an unexpected correlation between the mutated gene and the associated phenotype. This provides a good rationale for examining patients with apparent distal myopathy for a neuromuscular transmission disorder and agrin mutations.
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We demonstrate the use of a probabilistic generative model to explore the biomarker changes occurring as Alzheimer's disease develops and progresses. We enhanced the recently introduced event-based model for use with a multi-modal sporadic disease data set. This allows us to determine the sequence in which Alzheimer's disease biomarkers become abnormal without reliance on a priori clinical diagnostic information or explicit biomarker cut points. ⋯ The data-driven model we describe supports hypothetical models of biomarker ordering in amyloid-positive and APOE-positive subjects, but suggests that biomarker ordering in the wider population may diverge from this sequence. The model provides useful disease staging information across the full spectrum of disease progression, from cognitively normal to mild cognitive impairment to Alzheimer's disease. This approach has broad application across neurodegenerative disease, providing insights into disease biology, as well as staging and prognostication.