To systematically study somatic variants arising during development in the human brain across a spectrum of neurodegenerative disorders. In this study we developed a pipeline to identify somatic variants from exome sequencing data in 1461 diseased and control human brains. Eighty-eight percent of the DNA samples were extracted from the cerebellum. Identified somatic variants were validated by targeted amplicon sequencing and/or PyroMark® Q24. We observed somatic coding variants present in >10% of sampled cells in at least 1% of brains. The mutational signature of the detected variants showed a predominance of C>T variants most consistent with arising from DNA mismatch repair, occurred frequently in genes that are highly expressed within the central nervous system, and with a minimum somatic mutation rate of 4.25 × 10 per base pair per individual. These findings provide proof-of-principle that deleterious somatic variants can affect sizeable brain regions in at least 1% of the population, and thus have the potential to contribute to the pathogenesis of common neurodegenerative diseases.
To discern presymptomatic changes in brain structure or function using advanced MRI in carriers of mutations predisposing to amyotrophic lateral sclerosis (ALS). T1-weighted, diffusion weighted and resting state functional MRI data were acquired at 3 T for 12 asymptomatic mutation carriers (psALS), 12 age-matched controls and affected patients with ALS. Cortical thickness analysis, voxel-based morphometry, volumetric and shape analyses of subcortical structures, tract-based spatial statistics of metrics derived from the diffusion tensor, and resting state functional connectivity (FC) analyses were performed. Grey matter cortical thickness and shape analysis revealed significant atrophy in patients with ALS (but not psALS) compared with controls in the right primary motor cortex and right caudate. Comparison of diffusion tensor metrics showed widespread fractional anisotropy and radial diffusivity differences in patients with ALS compared to controls and the psALS group, encompassing parts of the corpus callosum, corticospinal tracts and superior longitudinal fasciculus. While FC in the resting-state sensorimotor network was similar in psALS and controls, FC between the cerebellum and a network comprising the precuneus, cingulate & middle frontal lobe was significantly higher in psALS and affected ALS compared to controls. Rather than structural brain changes, increased FC may be among the earliest detectable brain abnormalities in asymptomatic carriers of ALS-causing gene mutations. With replication and significant refinement, this technique has potential in the future assessment of neuroprotective strategies.
Amyotrophic lateral sclerosis (ALS) is characterized by progressive loss of upper and lower motor neurons. Advanced MRI techniques such as diffusion tensor imaging have shown great potential in capturing a common white matter pathology. However the sensitivity is variable and diffusion tensor imaging is not yet applicable to the routine clinical environment. Voxel-based morphometry (VBM) has revealed grey matter changes in ALS, but the bias-reducing algorithms inherent to traditional VBM are not optimized for the assessment of the white matter changes. We have developed a novel approach to white matter analysis, namely voxel-based intensitometry (VBI). High resolution T1-weighted MRI was acquired at 1.5 Tesla in 30 ALS patients and 37 age-matched healthy controls. VBI analysis at the group level revealed widespread white matter intensity increases in the corticospinal tracts, corpus callosum, sub-central, frontal and occipital white matter tracts and cerebellum. VBI results correlated with disease severity (ALSFRS-R) and patterns of cerebral involvement differed between bulbar- and limb-onset. VBI would be easily translatable to the routine clinical environment, and once optimized for individual analysis offers significant biomarker potential in ALS.
There is significant clinical and prognostic heterogeneity in the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), despite a common immunohistological signature. Consistent extra-motor as well as motor cerebral, spinal anterior horn and distal neuromuscular junction pathology supports the notion of ALS a system failure. Establishing a disease biomarker is a priority but a simplistic, coordinate-based approach to brain dysfunction using MRI is not tenable. Resting-state functional MRI reflects the organization of brain networks at the systems-level, and so changes in of motor functional connectivity were explored to determine their potential as the substrate for a biomarker signature. Intra- as well as inter-motor functional networks in the 0.03-0.06 Hz frequency band were derived from 40 patients and 30 healthy controls of similar age, and used as features for pattern detection, employing multiple kernel learning. This approach enabled an accurate classification of a group of patients that included a range of clinical sub-types. An average of 13 regions-of-interest were needed to reach peak discrimination. Subsequent analysis revealed that the alterations in motor functional connectivity were widespread, including regions not obviously clinically affected such as the cerebellum and basal ganglia. Complex network analysis showed that functional networks in ALS differ markedly in their topology, reflecting the underlying altered functional connectivity pattern seen in patients: 1) reduced connectivity of both the cortical and sub-cortical motor areas with non motor areas 2)reduced subcortical-cortical motor connectivity and 3) increased connectivity observed within sub-cortical motor networks. This type of analysis has potential to non-invasively define a biomarker signature at the systems-level. As the understanding of neurodegenerative disorders moves towards studying pre-symptomatic changes, there is potential for this type of approach to generate biomarkers for the testing of neuroprotective strategies.