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One of the roadblocks to developing effective therapeutics for Huntington disease (HD) is the lack of animal models that develop progressive clinical traits comparable to those seen in patients. Here we report a longitudinal study that encompasses cognitive and motor assessment, and neuroimaging of a group of transgenic HD and control monkeys from infancy to adulthood. Along with progressive cognitive and motor impairment, neuroimaging revealed a progressive reduction in striatal volume. Magnetic resonance spectroscopy at 48 months of age revealed a decrease of N-acetylaspartate (NAA), further suggesting neuronal damage/loss in the striatum. Postmortem neuropathological analyses revealed significant neuronal loss in the striatum. Our results indicate that HD monkeys share similar disease patterns with HD patients, making them potentially suitable as a preclinical HD animal model.

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

Purpose of Review Expanding therapeutic targets from proteins to RNAs opens up new possibilities for neurodegenerative disorders therapeutics development. Recently, a disease-modifying antisense oligonucleotide (ASO) agent was approved for spinal muscular atrophy, suggesting ASOs will fulfill their early promise and become a significant new therapeutic category for neurodegenerative disorders. Recent Findings ASOs are in human subjects testing for Huntington disease, monogenic forms of amyotrophic lateral sclerosis, Alzheimer disease, myotonic dystrophy, Leber congenital amaurosis, Usher syndrome, and retinitis pigmentosum, with many more in preclinical development. Current ASO strategies encompass RNA processing modulation, and RNA target breakdown. Broad ASO mechanism categories are protein restoring versus protein lowering. Individual ASO mechanisms of action range from mutation-specific to impacting many proteins. Summary Current ASOs show great promise in neurodegenerative disorders. Specific ASO designs and mechanisms may be more tenable in this disease area. Preclinical development is already leveraging early knowledge from these initial clinical trials to develop novel ASO cocktails, new ASO chemical modifications, and new ASO RNA and protein targets.

Huntington disease (HD) is a progressive neurodegenerative disease that causes psychiatric and neurological symptoms, including involuntary and irregular muscle movements (chorea). Chorea can disrupt activities of daily living, pose safety issues, and may lead to social withdrawal. The vesicular monoamine transporter 2 inhibitors tetrabenazine, deutetrabenazine, and valbenazine are approved treatments that can reduce chorea. This post hoc analysis was conducted to evaluate safety and efficacy among participants who received high-dosage deutetrabenazine treatment (> 48 mg/d) in ARC-HD, an open-label study that assessed long-term safety and efficacy of deutetrabenazine for the treatment of chorea in HD in adults. ARC-HD was a single-arm, two-cohort, open-label study. Participants either successfully completed the First-HD study or switched overnight from tetrabenazine to deutetrabenazine. Participants were dosed with deutetrabenazine in a response-driven manner (maximum 72 mg/d allowed). For the current analysis, exposure-adjusted incidence rates (EAIRs) for adverse events of interest were analyzed according to daily dosage (≤ 48 mg/d versus > 48 mg/d), and total maximal chorea (TMC) scores were analyzed by cohort during the stable-dose period. In total, 116 of the 119 participants enrolled in ARC-HD entered the stable-dose period, where no apparent differences were seen in EAIRs when receiving deutetrabenazine dosages ≤ 48 mg/d (exposure = 177.7 person-years) compared with > 48 mg/d (exposure = 74.1 person-years). Similar results were found among the subset of participants who received deutetrabenazine dosages > 48 mg/d at least once during the study (n = 49, 42%) when their dosage was ≤ 48 mg/d (exposure = 37.9 person-years) versus > 48 mg/d (74.1 person-years). Efficacy analyses were conducted for participants who had TMC scores available (rollover cohort, n = 77; switch cohort, n = 35). For most participants, the lowest deutetrabenazine dosage needed to achieve a TMC response (≥ 30% improvement from baseline) was between 24 and 48 mg/d in both the rollover (n = 57, 74.0%) and switch (n = 16, 46.0%) cohorts. Whereas the dosage needed for a TMC response was independent of baseline TMC score in the rollover cohort, participants with higher baseline TMC scores in the switch cohort required higher dosages to achieve a TMC response during the trial. In this open-label, long-term study, some participants received deutetrabenazine dosing > 48 mg/d to achieve adequate chorea control. There was no new safety concern or incremental change in the safety profile between dosages of ≤ 48 mg/d and > 48 mg/d. These results include dosages that have not been approved for clinical use, however, they increase our understanding of safety and tolerability of deutetrabenazine doses. ARC-HD (ClinicalTrials.gov identifier: NCT01897896); First-HD (ClinicalTrials.gov identifier: NCT01795859).

Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference ( p  > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.

The transcription factors in the myocyte enhancer factor 2 (MEF2) family play important roles in cell survival by regulating nuclear gene expression. Here, we report that MEF2D is present in rodent neuronal mitochondria, where it can regulate the expression of a gene encoded within mitochondrial DNA (mtDNA). Immunocytochemical, immunoelectron microscopic, and biochemical analyses of rodent neuronal cells showed that a portion of MEF2D was targeted to mitochondria via an N-terminal motif and the chaperone protein mitochondrial heat shock protein 70 (mtHsp70). MEF2D bound to a MEF2 consensus site in the region of the mtDNA that contained the gene NADH dehydrogenase 6 (ND6), which encodes an essential component of the complex I enzyme of the oxidative phosphorylation system; MEF2D binding induced ND6 transcription. Blocking MEF2D function specifically in mitochondria decreased complex I activity, increased cellular H(2)O(2) level, reduced ATP production, and sensitized neurons to stress-induced death. Toxins known to affect complex I preferentially disrupted MEF2D function in a mouse model of Parkinson disease (PD). In addition, mitochondrial MEF2D and ND6 levels were decreased in postmortem brain samples of patients with PD compared with age-matched controls. Thus, direct regulation of complex I by mitochondrial MEF2D underlies its neuroprotective effects, and dysregulation of this pathway may contribute to PD.

Diffusion-weighted imaging (DWI) is a key contrast mechanism in MRI which allows for the assessment of microstructural properties of brain tissues by measuring the displacement of water molecules. Several diffusion models, including the tensor (DTI), kurtosis (DKI), and neurite orientation dispersion and density imaging (NODDI), are commonly used in both research and clinical practice. However, there is currently no standardized method for validating the stability and repeatability of these models over time. This study evaluates the use of a DTI phantom as a standard reference for diffusion MRI model validation. The phantom, along with four healthy volunteers, was scanned repeatedly on different days to assess repeatability and stability. The acquired data were fitted to the diffusion models, with repeatability assessed in the phantom using the coefficient of variation (CoV), while stability in vivo was assessed using the repeatability coefficient (RC). The phantom was consecutively scanned eight times to investigate the impact of gradient coil heating on measurement consistency. Results showed that the phantom provided a highly reproducible reference, with CoVs below 5% across repeated and consecutive acquisitions, confirming the robustness of the diffusion models. In vivo, the low RCs indicated that the models remained stable over time, despite potential physiological variability. This study highlights the essential role of phantoms in diffusion MRI research, providing a reference framework for model validation. Future research will expand on this work to a multi-center study to assess inter-scanner variability, potentially incorporating the phantom into calibration protocols to standardize diffusion MRI measurements across different MRI systems.

Due to the extreme complexity of Alzheimer’s disease (AD), the etiology of which is not yet known, and for which there are no known effective treatments, mathematical modeling can be very useful. Indeed, mathematical models, if deemed reliable, can be used to test medical hypotheses that could be difficult to verify directly. In this context, it is important to understand how Aβ and τ proteins, which, in abnormal aggregate conformations, are hallmarks of the disease, interact and spread. We are particularly interested, in this paper, in studying the spreading of misfolded τ. To this end, we present four different mathematical models, all on networks on which the protein evolves. The models differ in both the choice of network and diffusion operator. Through comparison with clinical data on τ concentration, which we carefully obtained with multimodal analysis techniques, we show that some models are more adequate than others to simulate the dynamics of the protein. This type of study may suggest that, when it comes to modeling certain pathologies, the choice of the mathematical setting must be made with great care if comparison with clinical data is considered decisive.

Background The cingulum bundle is a brain white matter fasciculus associated with the cingulate gyrus. It connects areas from the temporal to the frontal lobe. It is composed of fibers with different terminations, lengths, and structural properties, related to specific brain functions. We aimed to automatically reconstruct this fasciculus in patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and to assess whether trajectories have different microstructural properties in relation to dementia progression. Methods Multi-shell high angular resolution diffusion imaging−HARDI image datasets from the \"Alzheimer's Disease Neuroimaging Initiative\"−ADNI repository of 10 AD, 18 MCI, and 21 cognitive normal (CN) subjects were used to reconstruct three subdivisions of the cingulum bundle, using a probabilistic approach, combined with measurements of diffusion tensor and neurite orientation dispersion and density imaging metrics in each subdivision. Results The subdivisions exhibit different pathways, terminations, and structural characteristics. We found differences in almost all the diffusivity metrics among the subdivisions ( p  < 0.001 for all the metrics) and between AD versus CN and MCI versus CN subjects for mean diffusivity ( p  = 0.007–0.038), radial diffusivity ( p  = 0.008–0.049) and neurite dispersion index ( p  = 0.005–0.049). Conclusion Results from tractography analysis of the subdivisions of the cingulum bundle showed an association in the role of groups of fibers with their functions and the variance of their properties in relation to dementia progression. Relevance statement The cingulum bundle is a complex tract with several pathways and terminations related to many cognitive functions. A probabilistic automatic approach is proposed to reconstruct its subdivisions, showing different microstructural properties and variations. A larger sample of patients is needed to confirm results and elucidate the role of diffusion parameters in characterizing alterations in brain function and progression to dementia. Key Points The microstructure of the cingulum bundle is related to brain cognitive functions. A probabilistic automatic approach is proposed to reconstruct the subdivisions of the cingulum bundle by diffusion-weighted images. The subdivisions showed different microstructural properties and variations in relation to the progression of dementia. Graphical Abstract

Kari Stefansson and colleagues report association of a variant in LINGO1 with risk of essential tremor, a common progressive neurological disease. Mice lacking Lingo1 have impaired axonal integrity, which may be relevant to the pathophysiology of the human disease. We identified a marker in LINGO1 showing genome-wide significant association ( P = 1.2 × 10 −9 , odds ratio = 1.55) with essential tremor. LINGO1 has potent, negative regulatory influences on neuronal survival and is also important in regulating both central-nervous-system axon regeneration and oligodendrocyte maturation. Increased axon integrity observed in Lingo1 mouse knockout models highlights the potential role of LINGO1 in the pathophysiology of essential tremor.