A set of glutamylases and deglutamylases controls levels of tubulin polyglutamylation, a prominent post-translational modification of neuronal microtubules. Defective tubulin polyglutamylation was first linked to neurodegeneration in the Purkinje cell degeneration (pcd) mouse, which lacks deglutamylase CCP1, displays massive cerebellar atrophy, and accumulates abnormally glutamylated tubulin in degenerating neurons. We found biallelic rare and damaging variants in the gene encoding CCP1 in 13 individuals with infantile-onset neurodegeneration and confirmed the absence of functional CCP1 along with dysregulated tubulin polyglutamylation. The human disease mainly affected the cerebellum, spinal motor neurons, and peripheral nerves. We also demonstrate previously unrecognized peripheral nerve and spinal motor neuron degeneration in pcd mice, which thus recapitulated key features of the human disease. Our findings link human neurodegeneration to tubulin polyglutamylation, entailing this post-translational modification as a potential target for drug development for neurodegenerative disorders.

Objective:To determine if following a Mediterranean-like diet (MeDi) relates to cognitive functions and in vivo biomarkers for Alzheimer’s disease (AD), we analyzed cross-sectional data from the German Longitudinal Cognitive Impairment and Dementia StudyMethod:The sample (n=512, mean age: 69.5±5.9 years) included 169 cognitively normal participants and subjects at higher AD risk (53 AD relatives, 209 SCD and 81 MCI). We defined MeDi adherence based on the Food Frequency Questionnaire. Brain volume outcomes were generated via voxel-based morphometry on T1-MRI and cognitive performance with an extensive neuropsychological battery. AD-related biomarkers (Aβ42/40 ratio, pTau181) in cerebrospinal fluid were assessed in n=226 individuals. We analyzed the associations between MeDi and the outcomes with linear regression models controlling for several covariates. Additionally, we applied hypothesis-driven mediation and moderation analysis.Results:Higher MeDi adherence related to larger mediotemporal gray matter volume (p<0.05 FWE corrected), better memory (β±SE = 0.03 ± 0.02; p=0.038), and less amyloid (Aβ42/40 ratio, β±SE = 0.003 ± 0.001; p=0.008) and pTau181 pathology (β±SE = -1.96±0.68; p=0.004). Mediotemporal volume mediated the association between MeDi and memory (40% indirect mediation). Finally, MeDi favorably moderated the associations between Aβ42/40 ratio, pTau181 and mediotemporal atrophy. Results were consistent correcting for ApoE-ε4 status.Conclusion:Our findings corroborate the view of MeDi as a protective factor against memory decline and mediotemporal atrophy. Importantly, they suggest that these associations might be explained by a decrease of amyloidosis and tau-pathology. Longitudinal and dietary intervention studies should further examine this conjecture and its treatment implications.

Purpose Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology. Methods When designing AI-based research in neuroradiology and appreciating the literature, it is important to understand the fundamental principles of AI. Training, validation, and test datasets must be defined and set apart as priorities. External validation and testing datasets are preferable, when feasible. The specific type of learning process (supervised vs. unsupervised) and the machine learning model also require definition. Deep learning (DL) is an AI-based approach that is modelled on the structure of neurons of the brain; convolutional neural networks (CNN) are a commonly used example in neuroradiology. Results Radiomics is a frequently used approach in which a multitude of imaging features are extracted from a region of interest and subsequently reduced and selected to convey diagnostic or prognostic information. Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features. Conclusion Common limitations and pitfalls in AI-based research in neuroradiology are limited sample sizes (“small-n-large-p problem”), selection bias, as well as overfitting and underfitting.