Longitudinal Effects of Asymptomatic C9orf72 Carriership on Brain Morphology.

OBJECTIVE: We investigated effects of C9orf72 repeat expansion and gene expression on longitudinal cerebral changes before symptom onset. METHODS: We enrolled 79 asymptomatic family members (AFMs) from 9 families with C9orf72 repeat expansion. Twenty-eight AFMs carried the mutation (C9+). Participants had up to 3 magnetic resonance imaging (MRI) scans, after which we compared motor cortex and motor tracts between C9+ and C9- AFMs using mixed effects models, incorporating kinship to correct for familial relations and lessen effects of other genetic factors. We also compared cortical, subcortical, cerebellar, and connectome structural measurements in a hypothesis-free analysis. We correlated regional C9orf72 expression in donor brains with the pattern of cortical thinning in C9+ AFMs using meta-regression. For comparison, we included 42 C9+ and 439 C9- patients with amyotrophic lateral sclerosis (ALS) in this analysis. RESULTS: C9+ AFM motor cortex had less gyrification and was thinner than in C9- AFMs, without differences in motor tracts. Whole brain analysis revealed thinner cortex and less gyrification in parietal, occipital, and temporal regions, smaller thalami and right hippocampus, and affected frontotemporal connections. Thinning of bilateral precentral, precuneus, and left superior parietal cortex was faster in C9+ than in C9- AFMs. Higher C9orf72 expression correlated with thinner cortex in both C9+ AFMs and C9+ ALS patients. INTERPRETATION: In asymptomatic C9orf72 repeat expansion carriers, brain MRI reveals widespread features suggestive of impaired neurodevelopment, along with faster decline of motor and parietal cortex than found in normal aging. C9orf72 expression might play a role in cortical development, and consequently explain the specific brain abnormalities of mutation carriers. ANN NEUROL 2023;93:668-680.

MRI Clustering Reveals Three ALS Subtypes With Unique Neurodegeneration Patterns.

OBJECTIVE: The purpose of this study was to identify subtypes of amyotrophic lateral sclerosis (ALS) by comparing patterns of neurodegeneration using brain magnetic resonance imaging (MRI) and explore their phenotypes. METHODS: We performed T1-weighted and diffusion tensor imaging in 488 clinically well-characterized patients with ALS and 338 control subjects. Measurements of whole-brain cortical thickness and white matter connectome fractional anisotropy were adjusted for disease-unrelated variation. A probabilistic network-based clustering algorithm was used to divide patients into subgroups of similar neurodegeneration patterns. Clinical characteristics and cognitive profiles were assessed for each subgroup. In total, 512 follow-up scans were used to validate clustering results longitudinally. RESULTS: The clustering algorithm divided patients with ALS into 3 subgroups of 187, 163, and 138 patients. All subgroups displayed involvement of the precentral gyrus and are characterized, respectively, by (1) pure motor involvement (pure motor cluster [PM]), (2) orbitofrontal and temporal involvement (frontotemporal cluster [FT]), and (3) involvement of the posterior cingulate cortex, parietal white matter, temporal operculum, and cerebellum (cingulate-parietal-temporal cluster [CPT]). These subgroups had significantly distinct clinical profiles regarding male-to-female ratio, age at symptom onset, and frequency of bulbar symptom onset. FT and CPT revealed higher rates of cognitive impairment on the Edinburgh cognitive and behavioral ALS screen (ECAS). Longitudinally, clustering remained stable: at 90.4% of their follow-up visits, patients clustered in the same subgroup as their baseline visit. INTERPRETATION: ALS can manifest itself in 3 main patterns of cerebral neurodegeneration, each associated with distinct clinical characteristics and cognitive profiles. Besides the pure motor and frontotemporal dementia (FTD)-like variants of ALS, a new neuroimaging phenotype has emerged, characterized by posterior cingulate, parietal, temporal, and cerebellar involvement. ANN NEUROL 2022;92:1030-1045.

Sensitivity of brain MRI and neurological examination for detection of upper motor neurone degeneration in amyotrophic lateral sclerosis.

OBJECTIVES: To investigate sensitivity of brain MRI and neurological examination for detection of upper motor neuron (UMN) degeneration in patients with amyotrophic lateral sclerosis (ALS). METHODS: We studied 192 patients with ALS and 314 controls longitudinally. All patients visited our centre twice and underwent full neurological examination and brain MRI. At each visit, we assessed UMN degeneration by measuring motor cortex thickness (CT) and pyramidal tract fibre density (FD) corresponding to five body regions (bulbar region and limbs). For each body region, we measured degree of clinical UMN and lower motor neuron (LMN) symptom burden using a validated scoring system. RESULTS: We found deterioration over time of CT of motor regions (p≤0.0081) and progression of UMN signs of bulbar region and left arm (p≤0.04). FD was discriminative between controls and patients with moderate/severe UMN signs (all regions, p≤0.034), but did not change longitudinally. Higher clinical UMN burden correlated with reduced CT, but not lower FD, for the bulbar region (p=2.2×10-10) and legs (p≤0.025). In the arms, we found that severe LMN signs may reduce the detectability of UMN signs (p≤0.043). With MRI, UMN degeneration was detectable before UMN signs became clinically evident (CT: p=1.1×10-10, FD: p=6.3×10-4). Motor CT, but not FD, deteriorated more than UMN signs during the study period. CONCLUSIONS: Motor CT is a more sensitive measure of UMN degeneration than UMN signs. Motor CT and pyramidal tract FD are discriminative between patients and controls. Brain MRI can monitor UMN degeneration before signs become clinically evident. These findings promote MRI as a potential biomarker for UMN progression in clinical trials in ALS.

Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis.

OBJECTIVE: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. METHODS: Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. RESULTS: We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. INTERPRETATION: Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725-738.