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.

The Distinct Traits of the UNC13A Polymorphism in Amyotrophic Lateral Sclerosis.

OBJECTIVE: The rs12608932 single nucleotide polymorphism in UNC13A is associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) susceptibility, and may underlie differences in treatment response. We aimed to characterize the clinical, cognitive, behavioral, and neuroimaging phenotype of UNC13A in patients with ALS. METHODS: We included 2,216 patients with ALS without a C9orf72 mutation to identify clinical characteristics associated with the UNC13A polymorphism. A subcohort of 428 patients with ALS was used to study cognitive and behavioral profiles, and 375 patients to study neuroimaging characteristics. Associations were analyzed under an additive genetic model. RESULTS: Genotyping rs12608932 resulted in 854 A/A, 988 A/C, and 374 C/C genotypes. The C allele was associated with a higher age at symptom onset (median years A/A 63.5, A/C 65.6, and C/C 65.5; p < 0.001), more frequent bulbar onset (A/A 29.6%, A/C 31.8%, and C/C 43.1%; p < 0.001), higher incidences of ALS-FTD (A/A 4.3%, A/C 5.2%, and C/C 9.5%; p = 0.003), lower forced vital capacity at diagnosis (median percentage A/A 92.0, A/C 90.0, and C/C 86.5; p < 0.001), and a shorter survival (median in months A/A 33.3, A.C 30.7, and C/C 26.6; p < 0.001). UNC13A was associated with lower scores on ALS-specific cognition tests (means A/A 79.5, A/C 78.1, and C/C 76.6; p = 0.037), and more frequent behavioral disturbances (A/A 16.7%, A/C 24.4%, and C/C 27.7%; p = 0.045). Thinner left inferior temporal and right fusiform cortex were associated with the UNC13A single nucleotide polymorphism (SNP; p = 0.045 and p = 0.036). INTERPRETATION: Phenotypical distinctions associated with UNC13A make it an important factor to take into account in clinical trial design, studies on cognition and behavior, and prognostic counseling. ANN NEUROL 2020;88:796-806.

Prognostic value of weight loss in patients with amyotrophic lateral sclerosis: a population-based study.

OBJECTIVE: To determine the prevalence and prognostic value of weight loss (WL) prior to diagnosis in patients with amyotrophic lateral sclerosis (ALS). METHODS: We enrolled patients diagnosed with ALS between 2010 and 2018 in a population-based setting. At diagnosis, detailed information was obtained regarding the patient's disease characteristics, anthropological changes, ALS-related genotypes and cognitive functioning. Complete survival data were obtained. Cox proportional hazard models were used to assess the association between WL and the risk of death during follow-up. RESULTS: The data set comprised 2420 patients of whom 67.5% reported WL at diagnosis. WL occurred in 71.8% of the bulbar-onset and in 64.2% of the spinal-onset patients; the mean loss of body weight was 6.9% (95% CI 6.8 to 6.9) and 5.5% (95% CI 5.5 to 5.6), respectively (p<0.001). WL occurred in 35.1% of the patients without any symptom of dysphagia. WL is a strong independent predictor of survival, with a dose response relationship between the amount of WL and the risk of death: the risk of death during follow-up increased by 23% for every 10% increase in WL relative to body weight (HR 1.23, 95% CI 1.13 to 1.51, p<0.001). CONCLUSIONS: This population-based study shows that two-thirds of the patients with ALS have WL at diagnosis, which also occurs independent of dysphagia, and is related to survival. Our results suggest that WL is a multifactorial process that may differ from patient to patient. Gaining further insight in its underlying factors could prove essential for future therapeutic measures.

Multimodal longitudinal study of structural brain involvement in amyotrophic lateral sclerosis.

OBJECTIVE: To understand the progressive nature of amyotrophic lateral sclerosis (ALS) by investigating differential brain patterns of gray and white matter involvement in clinically or genetically defined subgroups of patients using cross-sectional, longitudinal, and multimodal MRI. METHODS: We assessed cortical thickness, subcortical volumes, and white matter connectivity from T1-weighted and diffusion-weighted MRI in 292 patients with ALS (follow-up: n = 150) and 156 controls (follow-up: n = 72). Linear mixed-effects models were used to assess changes in structural brain measurements over time in patients compared to controls. RESULTS: Patients with a C9orf72 mutation (n = 24) showed widespread gray and white matter involvement at baseline, and extensive loss of white matter integrity in the connectome over time. In C9orf72-negative patients, we detected cortical thinning of motor and frontotemporal regions, and loss of white matter integrity of connections linked to the motor cortex. Patients with spinal onset displayed widespread white matter involvement at baseline and gray matter atrophy over time, whereas patients with bulbar onset started out with prominent gray matter involvement. Patients with unaffected cognition or behavior displayed predominantly motor system involvement, while widespread cerebral changes, including frontotemporal regions with progressive white matter involvement over time, were associated with impaired behavior or cognition. Progressive loss of gray and white matter integrity typically occurred in patients with shorter disease durations (<13 months), independent of progression rate. CONCLUSIONS: Heterogeneity of phenotype and C9orf72 genotype relates to distinct patterns of cerebral degeneration. We demonstrate that imaging studies have the potential to monitor disease progression and early intervention may be required to limit cerebral degeneration.

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.

Cross-sectional and longitudinal assessment of the upper cervical spinal cord in motor neuron disease.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease characterized by both upper and lower motor neuron degeneration. While neuroimaging studies of the brain can detect upper motor neuron degeneration, these brain MRI scans also include the upper part of the cervical spinal cord, which offers the possibility to expand the focus also towards lower motor neuron degeneration. Here, we set out to investigate cross-sectional and longitudinal disease effects in the upper cervical spinal cord in patients with ALS, progressive muscular atrophy (PMA: primarily lower motor neuron involvement) and primary lateral sclerosis (PLS: primarily upper motor neuron involvement), and their relation to disease severity and grey and white matter brain measurements. METHODS: We enrolled 108 ALS patients without C9orf72 repeat expansion (ALS C9-), 26 ALS patients with C9orf72 repeat expansion (ALS C9+), 28 PLS patients, 56 PMA patients and 114 controls. During up to five visits, longitudinal T1-weighted brain MRI data were acquired and used to segment the upper cervical spinal cord (UCSC, up to C3) and individual cervical segments (C1 to C4) to calculate cross-sectional areas (CSA). Using linear (mixed-effects) models, the CSA differences were assessed between groups and correlated with disease severity. Furthermore, a relationship between CSA and brain measurements was examined in terms of cortical thickness of the precentral gyrus and white matter integrity of the corticospinal tract. RESULTS: Compared to controls, CSAs at baseline showed significantly thinner UCSC in all groups in the MND spectrum. Over time, ALS C9- patients demonstrated significant thinning of the UCSC and, more specifically, of segment C3 compared to controls. Progressive thinning over time was also observed in C1 of PMA patients, while ALS C9+ and PLS patients did not show any longitudinal changes. Longitudinal spinal cord measurements showed a significant relationship with disease severity and we found a significant correlation between spinal cord and motor cortex thickness or corticospinal tract integrity for PLS and PMA, but not for ALS patients. DISCUSSION: Our findings demonstrate atrophy of the upper cervical spinal cord in the motor neuron disease spectrum, which was progressive over time for all but PLS patients. Cervical spinal cord imaging in ALS seems to capture different disease effects than brain neuroimaging. Atrophy of the cervical spinal cord is therefore a promising additional biomarker for both diagnosis and disease progression and could help in the monitoring of treatment effects in future clinical trials.

10Kin1day: A Bottom-Up Neuroimaging Initiative.

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

Early human brain development: insights into macroscale connectome wiring.

BACKGROUND: Early brain development is closely dictated by distinct neurobiological principles. Here, we aimed to map early trajectories of structural brain wiring in the neonatal brain. METHODS: We investigated structural connectome development in 44 newborns, including 23 preterm infants and 21 full-term neonates scanned between 29 and 45 postmenstrual weeks. Diffusion-weighted imaging data were combined with cortical segmentations derived from T2 data to construct neonatal connectome maps. RESULTS: Projection fibers interconnecting primary cortices and deep gray matter structures were noted to mature faster than connections between higher-order association cortices (fractional anisotropy (FA) F = 58.9, p < 0.001, radial diffusivity (RD) F = 28.8, p < 0.001). Neonatal FA-values resembled adult FA-values more than RD, while RD approximated the adult brain faster (F = 358.4, p < 0.001). Maturational trajectories of RD in neonatal white matter pathways revealed substantial overlap with what is known about the sequence of subcortical white matter myelination from histopathological mappings as recorded by early neuroanatomists (mean RD 68 regions r = 0.45, p = 0.008). CONCLUSION: Employing postnatal neuroimaging we reveal that early maturational trajectories of white matter pathways display discriminative developmental features of the neonatal brain network. These findings provide valuable insight into the early stages of structural connectome development.

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.