Understanding non-recreational prescription medication-sharing behaviours: a systematic review.

BACKGROUND: Prescription medication sharing refers to the lending or borrowing of prescription medications where the recipient is someone other than the person for whom the prescription is intended. Sharing prescription medication can cause significant harm. Adverse consequences include an increased risk of side effects, delayed health seeking, and severity of disease. Prevalence estimates vary across different populations and people's reasons for, and perceptions of risks from, sharing are poorly understood. AIM: To better understand prescription medication-sharing behaviours and practices - specifically, the prevalence, types of medications, reasons, perceived benefits and risks, and factors associated with medication sharing. DESIGN AND SETTING: This systematic review included primary studies in any setting, focusing on people who engage in medication sharing. METHOD: Electronic databases were searched from inception of databases to February 2023. RESULTS: In total, 19 studies were included. Prevalence of lifetime sharing ranged from 13% to 78%. All 19 studies reported that analgesics were the most shared, followed by antibiotics (n = 12) and allergy medication (n = 9). Common reasons for sharing were running out of medication (n = 7), cost (n = 7), and emergency (n = 6). Perceived benefits included resolution of the problem and convenience. Perceived risks included adverse drug reactions and misdiagnosis. Characteristics associated with sharing included age, female sex, having asthma, and unused medicines stored at home. CONCLUSION: Findings suggest that medication-sharing behaviour is common and involves a range of medicines for a variety of reasons. Data on the prevalence and predictors of prescription medication sharing are inconsistent. A better understanding of non-modifiable and potentially modifiable behavioural factors that contribute to sharing is needed to support development of effective interventions aimed at mitigating unsafe sharing practices.

Magnetic resonance brain volumetry biomarkers of CLN2 Batten disease identified with miniswine model.

Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (- 9.06 cm3), gray matter (- 4.37% 95 CI - 7.41; - 1.83), caudate (- 0.16%, 95 CI - 0.24; - 0.08) and putamen (- 0.11% 95 CI - 0.23; - 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ 3.42%, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (- 8.27%, 95 CI - 10.1; - 5.56) and cerebrospinal fluid (+ 6.88%, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation.

Role of Hounsfield Unit in Predicting Outcomes of Shock Wave Lithotripsy for Renal Calculi: Outcomes of a Systematic Review.

PURPOSE OF REVIEW: Extracorporeal shock wave lithotripsy success rates depend on several stone and patient-related factors, one of which is stone density which is calculated on computed tomography scan in Hounsfield Units. Studies have shown inverse correlation between SWL success and HU; however, there remains considerable variation between studies. We performed a systematic review regarding the use of HU in SWL for renal calculi to consolidate the current evidence and address current knowledge gaps. RECENT FINDINGS: Database including MEDLINE, EMBASE, and Scopus were searched from inception through August 2022. Studies in English language analysing stone density/attenuation in adult patients undergoing SWL for renal calculi were included for assessment of Shockwave lithotripsy outcomes, use of stone attenuation to predict success, use of mean and peak stone density and Hounsfield unit density, determination of optimum cut-off values, nomograms/scoring systems, and assessment of stone heterogeneity. 28 studies with a total of 4,206 patients were included in this systematic review with sample size ranging from 30 to 385 patients. Male to female ratio was 1.8, with an average age of 46.3 years. Mean overall ESWL success was 66.5%. Stone size ranged from 4 to 30 mm in diameter. Mean stone density was used by two-third of the studies to predict the appropriate cut-off for SWL success, ranging from 750 to 1000 HU. Additional factors such as peak HU and stone heterogeneity index were also evaluated with variable results. Stone heterogeneity index was considered a better indicator for success in larger stones (cut-off value of 213) and predicting SWL stone clearance in one session. Prediction scores had been attempted, with researchers looking into combining stone density with other factors such as skin to stone distance, stone volume, and differing heterogeneity indices with variable results. Numerous studies demonstrate a link between shockwave lithotripsy outcomes and stone density. Hounsfield unit < 750 has been found to be associated with shockwave lithotripsy success, with likelihood of failure strongly associated with values over 1000. Prospective standardisation of Hounsfield unit measurement and predictive algorithm for shockwave lithotripsy outcome should be considered to strengthen future evidence and help clinicians in the decision making. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) database: CRD42020224647.

Comprehensive shape analysis of the cortex in Huntington's disease.

The striatum has traditionally been the focus of Huntington's disease research due to the primary insult to this region and its central role in motor symptoms. Beyond the striatum, evidence of cortical alterations caused by Huntington's disease has surfaced. However, findings are not coherent between studies which have used cortical thickness for Huntington's disease since it is the well-established cortical metric of interest in other diseases. In this study, we propose a more comprehensive approach to cortical morphology in Huntington's disease using cortical thickness, sulcal depth, and local gyrification index. Our results show consistency with prior findings in cortical thickness, including its limitations. Our comparison between cortical thickness and local gyrification index underscores the complementary nature of these two measures-cortical thickness detects changes in the sensorimotor and posterior areas while local gyrification index identifies insular differences. Since local gyrification index and cortical thickness measures detect changes in different regions, the two used in tandem could provide a clinically relevant measure of disease progression. Our findings suggest that differences in insular regions may correspond to earlier neurodegeneration and may provide a complementary cortical measure for detection of subtle early cortical changes due to Huntington's disease.

A Novel Porcine Model of CLN2 Batten Disease that Recapitulates Patient Phenotypes.

CLN2 Batten disease is a lysosomal disorder in which pathogenic variants in CLN2 lead to reduced activity in the enzyme tripeptidyl peptidase 1. The disease typically manifests around 2 to 4 years of age with developmental delay, ataxia, seizures, inability to speak and walk, and fatality between 6 and 12 years of age. Multiple Cln2 mouse models exist to better understand the etiology of the disease; however, these models are unable to adequately recapitulate the disease due to differences in anatomy and physiology, limiting their utility for therapeutic testing. Here, we describe a new CLN2R208X/R208X porcine model of CLN2 disease. We present comprehensive characterization showing behavioral, pathological, and visual phenotypes that recapitulate those seen in CLN2 patients. CLN2R208X/R208X miniswine present with gait abnormalities at 6 months of age, ERG waveform declines at 6-9 months, vision loss at 11 months, cognitive declines at 12 months, seizures by 15 months, and early death at 18 months due to failure to thrive. CLN2R208X/R208X miniswine also showed classic storage material accumulation and glial activation in the brain at 6 months, and cortical atrophy at 12 months. Thus, the CLN2R208X/R208X miniswine model is a valuable resource for biomarker discovery and therapeutic development in CLN2 disease.

Urinary Stones and Intervention Quality of Life (USIQoL): Development and Validation of a New Core Universal Patient-reported Outcome Measure for Urinary Calculi.

BACKGROUND: Urolithiasis has a significant impact on patients' health-related quality of life (HRQoL). OBJECTIVE: To develop a core patient-reported outcome measure (PROM) using modern psychometric methods to quantify the impact of urolithiasis and different treatments. DESIGN, SETTING, AND PARTICIPANTS: Adult patients with urinary calculi, attending urology departments, covering all index categories and treatment spectrum, participated during different development phases. The pilot instrument was created from potential items (phases 1 and 2) within the conceptual framework. The instrument was pretested (phase 3) and then underwent psychometric evaluation in two parts (phases 4 and 5). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The validity and reliability of the new PROM were assessed using Rasch measurement theory (RUMM 2030 statistical software) and traditional analyses. RESULTS AND LIMITATIONS: In total, 683 patients (median age 51 yr, range 18-92 yr) participated during different phases. The initial 60-item draft (five scales) was completed by 212 patients (phase 4). A revised 25-item draft was produced after removal of unstable items. In the second field test, the revised version was evaluated by 369 patients. This led to the final Urinary Stones and Intervention Quality of Life (USIQoL; 15 items) with summated logit scores. The PROM includes three scales: pain with physical health (six items), psychosocial health (seven items) and work performance (two items). Lower scores indicate better outcomes. Results demonstrate that USIQoL is reliable (r ≥ 0.8) and internally consistent (α ≥ 0.7), and has good construct validity (good hypothesised correlations, r > 0.3) and satisfactory sensitivity to change (p < 0.01). All scales demonstrated unidimensionality with good item fit and person separation indices. A limitation is that USIQoL was developed in the English language within the UK population. CONCLUSIONS: USIQoL is a short, unidimensional, valid, and reliable PROM for assessing the HRQoL impact of urinary calculi and treatments. It is expected to serve as a core PROM across the entire spectrum of urolithiasis. PATIENT SUMMARY: Kidney stones are a common condition for which various treatment options are available. The condition and treatments have a significant impact on a patient's quality of life. This can be measured objectively using a valid and reliable patient-reported outcome measure (PROM) developed using modern methods. We have developed a PROM that provides helpful and accurate measurement useful for all stakeholders.

Sleep Duration, Sleep Apnea, and Gray Matter Volume.

This study aimed to evaluate the effect of sleep duration on brain structures in the presence versus absence of sleep apnea in middle-aged and older individuals. The study investigated a population-based sample of 2,560 individuals, aged 49-80 years. The presence of sleep apnea and self-reported sleep duration were examined in relation to gray matter volume (GMV) in total and lobar brain regions. We identified ranges of sleep duration associated with maximal GMV using quadratic regression and bootstrap sampling. A significant quadratic association between sleep duration and GMV was observed in total and lobar brain regions of men with sleep apnea. In the fully adjusted model, optimal sleep durations associated with peak GMV between brain regions ranged from 6.7 to 7.0 hours. Shorter and longer sleep durations were associated with lower GMV in total and 4 sub-regions of the brain in men with sleep apnea.

Longitudinal subcortical segmentation with deep learning.

Longitudinal information is important for monitoring the progression of neurodegenerative diseases, such as Huntington's disease (HD). Specifically, longitudinal magnetic resonance imaging (MRI) studies may allow the discovery of subtle intra-subject changes over time that may otherwise go undetected because of inter-subject variability. For HD patients, the primary imaging-based marker of disease progression is the atrophy of subcortical structures, mainly the caudate and putamen. To better understand the course of subcortical atrophy in HD and its correlation with clinical outcome measures, highly accurate segmentation is important. In recent years, subcortical segmentation methods have moved towards deep learning, given the state-of-the-art accuracy and computational efficiency provided by these models. However, these methods are not designed for longitudinal analysis, but rather treat each time point as an independent sample, discarding the longitudinal structure of the data. In this paper, we propose a deep learning based subcortical segmentation method that takes into account this longitudinal information. Our method takes a longitudinal pair of 3D MRIs as input, and jointly computes the corresponding segmentations. We use bi-directional convolutional long short-term memory (C-LSTM) blocks in our model to leverage the longitudinal information between scans. We test our method on the PREDICT-HD dataset and use the Dice coefficient, average surface distance and 95-percent Hausdorff distance as our evaluation metrics. Compared to cross-sectional segmentation, we improve the overall accuracy of segmentation, and our method has more consistent performance across time points. Furthermore, our method identifies a stronger correlation between subcortical volume loss and decline in the total motor score, an important clinical outcome measure for HD.

MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs.

The subcortical structures of the brain are relevant for many neurodegenerative diseases like Huntington's disease (HD). Quantitative segmentation of these structures from magnetic resonance images (MRIs) has been studied in clinical and neuroimaging research. Recently, convolutional neural networks (CNNs) have been successfully used for many medical image analysis tasks, including subcortical segmentation. In this work, we propose a 2-stage cascaded 3D subcortical segmentation framework, with the same 3D CNN architecture for both stages. Attention gates, residual blocks and output adding are used in our proposed 3D CNN. In the first stage, we apply our model to downsampled images to output a coarse segmentation. Next, we crop the extended subcortical region from the original image based on this coarse segmentation, and we input the cropped region to the second CNN to obtain the final segmentation. Left and right pairs of thalamus, caudate, pallidum and putamen are considered in our segmentation. We use the Dice coefficient as our metric and evaluate our method on two datasets: the publicly available IBSR dataset and a subset of the PREDICT-HD database, which includes healthy controls and HD subjects. We train our models on only healthy control subjects and test on both healthy controls and HD subjects to examine model generalizability. Compared with the state-of-the-art methods, our method has the highest mean Dice score on all considered subcortical structures (except the thalamus on IBSR), with more pronounced improvement for HD subjects. This suggests that our method may have better ability to segment MRIs of subjects with neurodegenerative disease.

Mild Cognitive Impairment as an Early Landmark in Huntington's Disease.

As one of the clinical triad in Huntington's disease (HD), cognitive impairment has not been widely accepted as a disease stage indicator in HD literature. This work aims to study cognitive impairment thoroughly for prodromal HD individuals with the data from a 12-year observational study to determine whether Mild Cognitive Impairment (MCI) in HD gene-mutation carriers is a defensible indicator of early disease. Prodromal HD gene-mutation carriers evaluated annually at one of 32 worldwide sites from September 2002 to April 2014 were evaluated for MCI in six cognitive domains. Linear mixed-effects models were used to determine age-, education-, and retest-adjusted cut-off values in cognitive assessment for MCI, and then the concurrent and predictive validity of MCI was assessed. Accelerated failure time (AFT) models were used to determine the timing of MCI (single-, two-, and multiple-domain), and dementia, which was defined as MCI plus functional loss. Seven hundred and sixty-eight prodromal HD participants had completed all six cognitive tasks, had MRI, and underwent longitudinal assessments. Over half (i.e., 54%) of the participants had MCI at study entry, and half of these had single-domain MCI. Compared to participants with intact cognitive performances, prodromal HD with MCI had higher genetic burden, worsened motor impairment, greater brain atrophy, and a higher likelihood of estimated HD onset. Prospective longitudinal study of those without MCI at baseline showed that 48% had MCI in subsequent visits and data visualization suggested that single-domain MCI, two-domain MCI, and dementia represent appropriate cognitive impairment staging for HD gene-mutation carriers. Findings suggest that MCI represents an early landmark of HD and may be a sensitive enrichment variable or endpoint for prodromal clinical trials of disease modifying therapeutics.

The ANTsX ecosystem for quantitative biological and medical imaging.

The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

COVID-19 (SARS-CoV-2) in Non-Airborne body fluids: A systematic review & Meta-analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been predominantly respiratory. This study aimed to evaluate the presence of virus in non-airborne body fluids as transmission vehicles. Medline, EMBASE, and Cochrane Library databases were searched from December 01, 2019, to July 01, 2020, using terms relating to SARS-CoV-2 and non-airborne clinical sample sources (feces, urine, blood, serum, serum, and peritoneum). Studies in humans, of any design, were included. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy 2 tool. Preferred Reporting Items for Systematic Reviews & Meta-Analyses) guidelines were used for abstracting data. If ≥5 studies reported proportions for the same non-respiratory site, a meta-analysis was conducted using either a fixed or random-effects model, depending on the presence of heterogeneity. A total of 22 studies with 648 patients were included. Most were cross-sectional and cohort studies. The SARS-CoV-2 RNA was most frequently detected in feces. Detectable RNA was reported in 17% of the blood samples, 8% of the serum, 16% in the semen, but rarely in urine. Prevalence of SARS-CoV-2 in non-airborne sites varies widely with a third of non-airborne fluids. Patients with bowel and non-specific symptoms have persistence of virus in feces for upto 2 weeks after symptom resolution. Although there was a very low detection rate in urine, given the more frequent prevalence in blood samples, the presence of SARS-CoV-2 in patients with disrupted urothelium or undergoing urinary tract procedures, is likely to be higher. Healthcare providers need to consider non-airborne transmission and persistence of SARS-CoV-2 in body fluids to enable appropriate precautions to protect healthcare workers and carers.

Guidance impact on primary care prescribing rates of simple analgesia: an interrupted time series analysis in England.

BACKGROUND: In March 2018, NHS England published guidance for clinical commissioning groups (CCGs) to encourage implementation of policy to reduce primary care prescriptions of over-the-counter medications, including simple analgesia. AIM: To investigate the impact of guidance publication on prescribing rates of simple analgesia (oral paracetamol, oral ibuprofen, and topical non-steroidal anti-inflammatory drugs) in primary care; CCG guidance implementation intentions; and whether the guidance has created health inequality based on socioeconomic status. DESIGN AND SETTING: Interrupted time series analysis of primary care prescribing data in England. METHOD: Practice-level prescribing data from January 2015 to March 2019 were obtained from NHS Digital. Interrupted time series analyses were used to assess the association of guidance publication with prescribing rates. The association between practice-level prescribing rates and Index of Multiple Deprivation scores before and after publication was quantified using multivariable Poisson regression. Freedom of information requests were submitted to all CCGs. RESULTS: There was a statistically significant 4.4% reduction in prescribing of simple analgesia following guidance publication (adjusted incidence rate ratio 0.96, 95% CI = 0.92 to 0.99, P = 0.027), adjusting for underlying time trend and seasonality. There was considerable diversity across CCGs in whether or how they chose to implement the guidance. Practice-level prescribing rates were greater in more deprived areas. CONCLUSION: Guidance publication was associated with a small reduction in the prescribing rates of simple analgesia across England, without evidence of creating additional health inequality. Careful implementation by CCGs would be required to optimise cost saving to the NHS.

SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization.

PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Robust Markers and Sample Sizes for Multicenter Trials of Huntington Disease.

OBJECTIVE: The identification of sensitive biomarkers is essential to validate therapeutics for Huntington disease (HD). We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD. METHODS: We used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease-affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers. RESULTS: We identified a set of common anatomical regions that have similarly large standardized effect sizes (>0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of >80% was achieved for the caudate (n = 661), pallidum (n = 687), and nonventricular CSF (n = 939), and, crucially, these imaging markers provided greater power than standard clinical markers. INTERPRETATION: Our findings provide the first cross-study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. ANN NEUROL 2020;87:751-762.

Generalizing MRI Subcortical Segmentation to Neurodegeneration.

Many neurodegenerative diseases like Huntington's disease (HD) affect the subcortical structures of the brain, especially the caudate and the putamen. Automated segmentation of subcortical structures from MRI scans is thus important in HD studies. LiviaNET [2] is the state-of-the-art deep learning approach for subcortical segmentation. As all learning-based models, this approach requires appropriate training data. While annotated healthy control images are relatively easy to obtain, generating such annotations for each new disease population can be prohibitively expensive. In this work, we explore LiviaNET variants using well-known strategies for improving performance, to make it more generalizable to patients with substantial neurodegeneration. Specifically, we explored Res-blocks in our convolutional neural network, and we also explored manipulating the input to the network as well as random elastic deformations for data augmentation. We tested our method on images from the PREDICT-HD dataset, which includes control and HD subjects. We trained on control subjects and tested on both controls and HD patients. Compared to the original LiviaNET, we improved the accuracy of most structures, both for controls and for HD patients. The caudate has the most pronounced improvement in HD subjects with the proposed modifications to LiviaNET, which is noteworthy since caudate is known to be severely atrophied in HD. This suggests our extensions may improve the generalization ability of LiviaNET to cohorts where significant neurodegeneration is present, without needing to be retrained.

TENSOR-BASED GRADING: A NOVEL PATCH-BASED GRADING APPROACH FOR THE ANALYSIS OF DEFORMATION FIELDS IN HUNTINGTON'S DISEASE.

The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Among these, the patch-based grading framework has been proposed to model local patterns of anatomical changes. This approach is attractive because of its low computational cost and its competitive performance. Other studies have proposed to analyze the deformations of brain structures using tensor-based morphometry, which is a highly interpretable approach. In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric. We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls. Our experiments show a substantial increase in classification accuracy (87.5 ± 0.5 vs. 81.3 ± 0.6) compared to the existing patch-based grading methods, and a good complement to putamen volume, which is a primary imaging-based marker for the study of Huntington's disease.

Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington's Disease.

Deep learning techniques have demonstrated state-of-the-art performances in many medical imaging applications. These methods can efficiently learn specific patterns. An alternative approach to deep learning is patch-based grading methods, which aim to detect local similarities and differences between groups of subjects. This latter approach usually requires less training data compared to deep learning techniques. In this work, we propose two major contributions: first, we combine patch-based and deep learning methods. Second, we propose to extend the patch-based grading method to a new patch-based abnormality metric. Our method enables us to detect localized structural abnormalities in a test image by comparison to a template library consisting of images from a variety of healthy controls. We evaluate our method by comparing classification performance using different sets of features and models. Our experiments show that our novel patch-based abnormality metric increases deep learning performance from 91.3% to 95.8% of accuracy compared to standard deep learning approaches based on the MRI intensity.

Correction to: SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research.

This paper had published originally without open access, but has since been republished with open access.

Association of CAG Repeats With Long-term Progression in Huntington Disease.

IMPORTANCE: In Huntington disease (HD), mutation severity is defined by the length of the CAG trinucleotide sequence, a well-known predictor of clinical onset age. The association with disease trajectory is less well characterized. Quantifiable summary measures of trajectory applicable over decades of early disease progression are lacking. An accurate model of the age-CAG association with early progression is critical to clinical trial design, informing both sample size and intervention timing. OBJECTIVE: To succinctly capture the decades-long early progression of HD and its dependence on CAG repeat length. DESIGN, SETTING, AND PARTICIPANTS: Prospective study at 4 academic HD treatment and research centers. Participants were the combined sample from the TRACK-HD and Track-On HD studies consisting of 290 gene carriers (presymptomatic to stage II), recruited from research registries at participating centers, and 153 nonbiologically related controls, generally spouses or friends. Recruitment was targeted to match a balanced, prespecified spectrum of age, CAG repeat length, and diagnostic status. In the TRACK-HD and Track-On HD studies, 13 and 5 potential participants, respectively, failed study screening. Follow-up ranged from 0 to 6 years. The study dates were January 2008 to November 2014. These analyses were performed between December 2015 and January 2019. MAIN OUTCOMES AND MEASURES: The outcome measures were principal component summary scores of motor-cognitive function and of brain volumes. The main outcome was the association of these scores with age and CAG repeat length. RESULTS: We analyzed 2065 visits from 443 participants (247 female [55.8%]; mean [SD] age, 44.4 [10.3] years). Motor-cognitive measures were highly correlated and had similar CAG repeat length-dependent associations with age. A composite summary score accounted for 67.6% of their combined variance. This score was well approximated by a score combining 3 items (total motor score, Symbol Digit Modalities Test, and Stroop word reading) from the Unified Huntington's Disease Rating Scale. For either score, initial progression age and then acceleration rate were highly CAG repeat length dependent. The acceleration continues through at least stage II disease. In contrast, 3 distinct patterns emerged among brain measures (basal ganglia, gray matter, and a combination of whole-brain, ventricular, and white matter volumes). The basal ganglia pattern showed considerable change in even the youngest participants but demonstrated minimal acceleration of loss with aging. Each clinical and brain summary score was strongly associated with the onset and rate of decline in total functional capacity. CONCLUSIONS AND RELEVANCE: Results of this study suggest that succinct summary measures of function and brain loss characterize HD progression across a wide disease span. CAG repeat length strongly predicts their decline rate. This work aids our understanding of the age and CAG repeat length-dependent association between changes in the brain and clinical manifestations of HD.