BACKGROUND AND PURPOSE: The “ears of the lynx” MR imaging sign has been described in case reports of hereditary spastic paraplegia with a thin corpus callosum, mostly associated with mutations in the spatacsin vesicle trafficking associated gene, causing Spastic Paraplegia type 11 (SPG11). This sign corresponds to long T1 and T2 values in the forceps minor of the corpus callosum, which appears hyperintense on FLAIR and hypointense on T1-weighted images. Our purpose was to determine the sensitivity and specificity of the ears of the lynx MR imaging sign for genetic cases compared with common potential mimics. MATERIALS AND METHODS: Four independent raters, blinded to the diagnosis, determined whether the ears of the lynx sign was present in each of a set of 204 single anonymized FLAIR and T1-weighted MR images from 34 patients with causal mutations associated with SPG11 or Spastic Paraplegia type 15 (SPG15). 34 healthy controls, and 34 patients with multiple sclerosis. RESULTS: The interrater reliability for FLAIR images was substantial (Cohen κ, 0.66–0.77). For these images, the sensitivity of the ears of the lynx sign across raters ranged from 78.8 to 97.0 and the specificity ranged from 90.9 to 100. The accuracy of the sign, measured by area under the receiver operating characteristic curve, ranged from very good (87.1) to excellent (93.9). CONCLUSIONS: The ears of the lynx sign on FLAIR MR imaging is highly specific for the most common genetic subtypes of hereditary spastic paraplegia with a thin corpus callosum. When this sign is present, there is a high likelihood of a genetic mutation, particularly associated with SPG11 or SPG15, even in the absence of a family history.

Background: Huntington’s disease (HD) is a genetic, neurodegenerative disease. Due to the progressive nature of HD and the absence of a cure, (health-related) quality of life ((HR)QoL) is an important topic. Several studies have investigated (HR)QoL in HD, yet a clear synthesis of the existing literature is lacking to date. We performed a systematic review on self-reported (HR)QoL, and factors and intervention effects associated with (HR)QoL in premanifest and manifest HD gene expansion carriers (pHDGECs and mHDGECs, respectively). Methods: PubMed, EMBASE, Web of Science, and PsycINFO were searched systematically from September 17th, 2021, up to August 11th, 2022. Methodological and conceptual quality of the included studies was assessed with two appraisal tools. Results: 30 out of 70 eligible articles were included. mHDGECs experienced lower (HR)QoL compared to pHDGECs and controls, whereas mixed findings were reported when compared to other neurological diseases. Several factors were associated with (HR)QoL that might contribute to lower (HR)QoL in mHDGECs, including depressive symptoms, physical and psychological symptoms, lower functional capacity, lower support, and unmet needs. Multidisciplinary rehabilitation programs and a respiratory muscle training were beneficial for (HR)QoL in mHDGECs. Discussion: (HR)QoL is experienced differently across the course of the disease. Although (HR)QoL is key for understanding the impact of HD and the effect of symptomatic treatment, there is a need to improve the methodological and conceptual shortcomings that were found in most studies, especially regarding the conceptual clarity when reporting on QoL and HRQoL. Suggestions for strengthening these shortcomings are provided in this review.

Although SPG11 is the most common complicated hereditary spastic paraplegia, our knowledge of the long-term prognosis and life expectancy is limited. We therefore studied the disease course of all patients with a proven SPG11 mutation as tested in our laboratory, the single Dutch laboratory providing SPG11 mutation analysis, between 1 January 2009 and 1 January 2011. We identified nine different SPG11 mutations, four of which are novel, in nine index patients. Eighteen SPG11 patients from these nine families were studied by means of a retrospective chart analysis and additional interview/examination. Ages at onset were between 4 months and 14 years; 39% started with learning difficulties rather than gait impairment. Brain magnetic resonance imaging showed a thin corpus callosum and typical periventricular white matter changes in the frontal horn region (known as the ‘ears-of the lynx’-sign) in all. Most patients became wheelchair bound after a disease duration of 1 to 2 decades. End-stage disease consisted of loss of spontaneous speech, severe dysphagia, spastic tetraplegia with peripheral nerve involvement and contractures. Several patients died of complications between ages 30 and 48 years, 3–4 decades after onset of gait impairment. Other relevant features during the disease were urinary and fecal incontinence, obesity and psychosis. Our study of 18 Dutch SPG11-patients shows the potential serious long-term consequences of SPG11 including a possibly restricted life span.

Strong evidence suggests a significant role for iron accumulation in the brain in addition to the well-documented neurodegenerative aspects of Huntington's disease (HD). The putative mechanisms by which iron is linked to the HD pathogenesis are multiple, including oxidative stress, ferroptosis and neuroinflammation. However, no previous study in a neurodegenerative disease has linked the observed increase of brain iron accumulation as measured by MRI with well-established cerebrospinal fluid (CSF) and blood biomarkers for iron accumulation, or with associated processes such as neuroinflammation. This study is designed to link quantitative data from iron levels and neuroinflammation metabolites obtained from 7T MRI of HD patients, with specific and well-known clinical biofluid markers for iron accumulation, neurodegeneration and neuroinflammation. Biofluid markers will provide quantitative measures of overall iron accumulation, neurodegeneration and neuroinflammation, while MRI measurements on the other hand will provide quantitative spatial information on brain pathology, neuroinflammation and brain iron accumulation, which will be linked to clinical outcome measures. This is an observational cross-sectional study, IMAGINE-HD, in HD gene expansion carriers and healthy controls. We include premanifest HD gene expansion carriers and patients with manifest HD in an early or moderate stage. The study includes a 7T MRI scan of the brain, clinical evaluation, motor, functional, and neuropsychological assessments, and sampling of CSF and blood for the detection of iron, neurodegenerative and inflammatory markers. Quantitative Susceptibility Maps will be reconstructed using T2* weighted images to quantify brain iron levels and Magnetic Resonance Spectroscopy will be used to obtain information about neuroinflammation by measuring cell-specific intracellular metabolites' level and diffusion. Age and sex matched healthy subjects are included as a control group. Results from this study will provide an important basis for the evaluation of brain iron levels and neuroinflammation metabolites as an imaging biomarker for disease stage in HD and their relationship with the salient pathomechanisms of the disease on the one hand, and with clinical outcome on the other.

Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main. Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.