Association between Age and Striatal Volume Stratified by CAG Repeat Length in Prodromal Huntington Disease

Optimizing Screening for Intrastriatal Interventions in Huntington's Disease Using Predictive Models

BACKGROUND

Intrastriatal delivery of potential therapeutics in Huntington's disease (HD) requires sufficient caudate and putamen volumes. Currently, volumetric magnetic resonance imaging is rarely done in clinical practice, and these data are not available in large research cohorts such as Enroll-HD.

OBJECTIVE

The objective of this study was to investigate whether predictive models can accurately classify HD patients who exceed caudate and putamen volume thresholds required for intrastriatal therapeutic interventions.

METHODS

We obtained and merged data for 1374 individuals across three HD cohorts: IMAGE-HD, PREDICT-HD, and TRACK-HD/TRACK-ON. We imputed missing data for clinical variables with >72% non-missing values and used the model-building algorithm BORUTA to identify the 10 most important variables. A random forest algorithm was applied to build a predictive model for putamen volume >2500 mm3 and caudate volume >2000 mm3 bilaterally. Using the same 10 predictors, we constructed a logistic regression model with predictors significant at P < 0.05.

RESULTS

The random forest model with 1000 trees and minimal terminal node size of 5 resulted in 83% area under the curve (AUC). The logistic regression model retaining age, CAG repeat size, and symbol digit modalities test-correct had 85.1% AUC. A probability cutoff of 0.8 resulted in 5.4% false positive and 66.7% false negative rates.

CONCLUSIONS

Using easily obtainable clinical data and machine learning-identified initial predictor variables, random forest, and logistic regression models can successfully identify people with sufficient striatal volumes for inclusion cutoffs. Adopting these models in prescreening could accelerate clinical trial enrollment in HD and other neurodegenerative disorders when volume cutoffs are necessary enrollment criteria. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Predictive Modeling of Huntington's Disease Unfolds Thalamic and Caudate Atrophy Dissociation

BACKGROUND

Atrophy in the striatum is a hallmark of Huntington's disease (HD), including the period before clinical motor diagnosis (before-CMD), but it extends to other subcortical structures. The study of the covariation of these structures could improve the detection of disease-related longitudinal progression before-CMD, provide mechanistic insights of the disease, and potentially be used to obtain accurate prospective estimates of atrophy before-CMD and early after-CMD.

METHODS

We analyzed data from 337 before-CMD individuals, 236 healthy control subjects, and 95 early after-CMD individuals from three studies, and we used nine subcortical regions volumes in two analyses. First, we discriminated before-CMD from healthy control trajectories by integrating volume changes from these regions. Second, we estimated prospective atrophy before-CMD and early after-CMD by considering the influence of a region's present volume over the future volume of another one.

RESULTS

Before-CMD progression was robustly detected across studies. Indeed, detection of before-CMD progression improved when multiple structures were integrated, as opposed to analyzing the striatum alone, likely because of the reduced partial correlation between caudate and thalamic volume change before-CMD. Our multivariate atrophy prediction model found a thalamus-caudate association that is consistent with this pattern, which yields an improved caudate atrophy prediction in early after-CMD.

CONCLUSIONS

This study is the first attempt to validate before-CMD multivariate subcortical change detection across studies and to do multivariate prospective atrophy prediction in HD. These models achieve improved performance by detecting a dissociation between caudate and thalamic atrophy trajectories, and they provide a possible mechanistic understanding of the dynamics of HD. © 2022 International Parkinson and Movement Disorder Society.

Moderate Intensity Exercise in Pre-manifest Huntington's Disease: Results of a 6 months Trial

BACKGROUND

While it has been shown that aerobic exercise interventions are well tolerated in participants with the Huntington disease (HD) gene mutation, no study to date has tested whether an aerobic exercise intervention benefits brain structure and function in pre-manifest HD.

OBJECTIVE

In this study we utilized magnetic resonance (MR) imaging techniques to assess the efficacy of moderate-to-vigorous exercise treatment relative to active stretching and toning control.

METHODS

Forty pre-manifest participants with confirmed HD gene expansion were recruited into a two-arm intervention study that included a moderate-to-vigorous intensity home-based walking exercise intervention (N=34) and an active stretching and toning control intervention (N=6). Participants were assessed at baseline and after 26 weeks in one of the two study arms.

RESULTS

25 of the 34 (74%) participants assigned to the moderate-to-vigorous intensity group completed the intervention while 4 of the 6 (67%) participants in the stretching and toning intervention completed the study. The primary analyses compared the two arms of the study and found no statistical differences between the groups. Both groups were found to have improved their cardiorespiratory fitness as assessed by maximal oxygen uptake (VO₂max). A secondary analysis combined the two arms of the study and there was a significant relationship (p<0.05) between change in VO₂max and change in brain structure.

CONCLUSIONS

Though this study did not show efficacy for the exercise intervention, secondary results suggest that aerobic exercise interventions increasing cardiorespiratory fitness may be a potential way to slow progression in pre-manifest HD.

Human Immunodeficiency Virus Infection in Huntington's Disease is Associated with an Earlier Age of Symptom Onset

BACKGROUND

Huntington Disease (HD) and human immunodeficiency virus (HIV) are both associated with neurodegeneration in the cerebral cortex and striatum. The rate of striatal degeneration is a known predictor of symptom onset in HD indicating a potential neurobiological link between HD and HIV.

OBJECTIVE

To determine if the presence of pre-existing HIV infection would trigger a significantly earlier age of symptom onset (ASO) in HD-mutation carriers when compared to non-infected HD subjects.

METHODS

This was a retrospective analysis of the Enroll-HD database that included participants with a CAG repeat of at least 36. Participants with HD and a comorbidity of HIV that was diagnosed prior to their reported ASO were identified and compared to participants with HD who did not have HIV. An ANCOVA analysis was performed to investigate the differences in ASO between the HIV and non-HIV groups. Sex, drug use, and CAG repeat number were used as covariates.

RESULTS

The average ASO of HD subjects with previous HIV infection (n = 8) was 9.1 years earlier than non-HIV infected HD subjects (n = 3259) [F (1, 3267) =10.05, p = 0.002]. Despite low numbers of participants in the HIV group, the calculated effect size of this difference was 1.07.

CONCLUSION

The known neurobiological changes caused by HIV seem to hasten the ASO in patients with HD. These results may enhance our understanding of the neuropathology of HD in a way that will help with the identification of novel targets for future therapies.

Apathy Is Related to Cognitive Control and Striatum Volumes in Prodromal Huntington's Disease

OBJECTIVES

Apathy is a debilitating symptom of Huntington's disease (HD) and manifests before motor diagnosis, making it an excellent therapeutic target in the preclinical phase of Huntington's disease (prHD). HD is a neurological genetic disorder characterized by cognitive and motor impairment, and psychiatric abnormalities. Apathy is not well characterized within the prHD. In previous literature, damage to the caudate and putamen has been correlated with increased apathy in other neurodegenerative and movement disorders. The objective of this study was to determine whether apathy severity in individuals with prHD is related to striatum volumes and cognitive control. We hypothesized that, within prHD individuals, striatum volumes and cognitive control scores would be related to apathy.

METHODS

We constructed linear mixed models to analyze striatum volumes and cognitive control, a composite measure that includes tasks assessing with apathy scores from 797 prHD participants. The outcome variable for each model was apathy, and the independent variables for the four separate models were caudate volume, putamen volume, cognitive control score, and motor symptom score. We also included depression as a covariate to ensure that our results were not solely related to mood.

RESULTS

Caudate and putamen volumes, as well as measures of cognitive control, were significantly related to apathy scores even after controlling for depression.

CONCLUSIONS

The behavioral apathy expressed by these individuals was related to regions of the brain commonly associated with isolated apathy, and not a direct result of mood symptoms. (JINS, 2019, 25, 462-469).

Altered brain iron content and deposition rate in Huntington's disease as indicated by quantitative susceptibility MRI

Altered brain iron content in the striatum of premanifest and manifest Huntington's disease (HD) has been reported. However, its natural history remains unclear. This study aims to investigate altered brain iron content in premanifest and early HD, and the iron deposition rate in these patients through a longitudinal one-year follow-up test, with quantitative magnetic susceptibility as an iron imaging marker. Twenty-four gene mutation carriers divided into three groups (further-from-onset, closer-to-onset and early HD) and 16 age-matched healthy controls were recruited at baseline, and of these, 14 carriers and 7 controls completed the one-year follow-up. Quantitative magnetic susceptibility and effective transverse relaxation rate ( R 2 ∗ ) were measured at 7.0 Tesla and correlated with atrophy and available clinical and cognitive measurements. Higher susceptibility values indicating higher iron content in the striatum and globus pallidus were only observed in closer-to-onset (N = 6, p < 0.05 in caudate and p < 0.01 in putamen) and early HD (N = 9, p < 0.05 in caudate and globus pallidus and p < 0.01 in putamen). Similar results were found by R 2 ∗ measurement. Such increases directly correlated with HD CAG-age product score and brain atrophy, but not with motor or cognitive scores. More importantly, a significantly higher iron deposition rate (11.9%/years in caudate and 6.1%/years in globus pallidus) was firstly observed in closer-to-onset premanifest HD and early HD as compared to the controls. These results suggest that monitoring brain iron may provide further insights into the pathophysiology of HD disease progression, and may provide a biomarker for clinical trials.

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

OBJECTIVE

Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real-life clinical diagnosis in HD.

METHOD

A multivariate machine learning approach was applied to resting-state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross-group comparisons between preHD and controls, and within the preHD group in relation to "estimated" and "actual" proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.

RESULTS

Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.

INTERPRETATION

We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532-543.

Optimization of trans-Splicing for Huntington's Disease RNA Therapy

Huntington's disease (HD) is a devastating neurodegenerative disorder caused by a polyglutamine (polyQ) expansion in exon 1 of the Huntingtin (HTT) gene. We have previously demonstrated that spliceosome-mediated trans-splicing is a viable molecular strategy to specifically reduce and repair mutant HTT (mtHTT). Here, the targeted tethering efficacy of the pre-mRNA trans-splicing modules (PTM) in HTT was optimized. Various PTMs that targeted the 3′ end of HTT intron 1 or the intron 1 branch point were shown trans-splice into an HTT mini-gene, as well as the endogenous HTT pre-mRNA. PTMs that specifically target the endogenous intron 1 branch point increased the trans-splicing efficacy from 1–5 to 10–15%. Furthermore, lentiviral expression of PTMs in a human HD patient iPSC-derived neural culture significantly reversed two previously established polyQ-length dependent phenotypes. These results suggest that pre-mRNA repair of mtHTT could hold therapeutic benefit and it demonstrates an alternative platform to correct the mRNA product produced by the mtHTT allele in the context of HD.

Structural imaging in premanifest and manifest Huntington disease

Huntington disease (HD) neuropathology has a devastating effect on brain structure and consequently brain function; neuroimaging provides a means to assess these effects in gene carriers. In this chapter we first outline the unique utility of structural imaging in understanding HD and discuss some of the acquisition and analysis techniques currently available. We review the existing literature to summarize what we know so far about structural brain changes across the spectrum of disease from premanifest through to manifest disease. We then consider how these neuroimaging findings relate to patient function and nonimaging biomarkers, and can be used to predict disease onset. Finally we review the utility of imaging measures for assessment of treatment efficacy in clinical trials.

Bayesian Covariate Selection in Mixed-Effects Models For Longitudinal Shape Analysis

The goal of longitudinal shape analysis is to understand how anatomical shape changes over time, in response to biological processes, including growth, aging, or disease. In many imaging studies, it is also critical to understand how these shape changes are affected by other factors, such as sex, disease diagnosis, IQ, etc. Current approaches to longitudinal shape analysis have focused on modeling age-related shape changes, but have not included the ability to handle covariates. In this paper, we present a novel Bayesian mixed-effects shape model that incorporates simultaneous relationships between longitudinal shape data and multiple predictors or covariates to the model. Moreover, we place an Automatic Relevance Determination (ARD) prior on the parameters, that lets us automatically select which covariates are most relevant to the model based on observed data. We evaluate our proposed model and inference procedure on a longitudinal study of Huntington's disease from PREDICT-HD. We first show the utility of the ARD prior for model selection in a univariate modeling of striatal volume, and next we apply the full high-dimensional longitudinal shape model to putamen shapes.

Huntington disease

Huntington disease is devastating to patients and their families - with autosomal dominant inheritance, onset typically in the prime of adult life, progressive course, and a combination of motor, cognitive and behavioural features. The disease is caused by an expanded CAG trinucleotide repeat (of variable length) in HTT, the gene that encodes the protein huntingtin. In mutation carriers, huntingtin is produced with abnormally long polyglutamine sequences that confer toxic gains of function and predispose the protein to fragmentation, resulting in neuronal dysfunction and death. In this Primer, we review the epidemiology of Huntington disease, noting that prevalence is higher than previously thought, geographically variable and increasing. We describe the relationship between CAG repeat length and clinical phenotype, as well as the concept of genetic modifiers of the disease. We discuss normal huntingtin protein function, evidence for differential toxicity of mutant huntingtin variants, theories of huntingtin aggregation and the many different mechanisms of Huntington disease pathogenesis. We describe the genetic and clinical diagnosis of the condition, its clinical assessment and the multidisciplinary management of symptoms, given the absence of effective disease-modifying therapies. We review past and present clinical trials and therapeutic strategies under investigation, including impending trials of targeted huntingtin-lowering drugs and the progress in development of biomarkers that will support the next generation of trials. For an illustrated summary of this Primer, visit: http://go.nature.com/hPMENh.

The role of iron in gray matter degeneration in Huntington's disease: a magnetic resonance imaging study

In Huntington's disease, iron accumulation in basal ganglia accompanies neuronal loss. However, if iron content changes with disease progression and how it relates to gray matter atrophy is not clear yet. We explored iron content in basal ganglia and cortex and its relationship with gray matter volume in 77 mutation carriers [19 presymptomatic, 8 with soft symptoms (SS), and 50 early-stage patients) and 73 matched-controls by T2*relaxometry and T1-weighted imaging on a 3T scanner. The ANCOVA model showed that iron accumulates in the caudate in presymptomatic subjects (P = 0.004) and remains relatively stable along disease stages in this nucleus; while increases in putamen and globus pallidus (P < 0.05). Volume instead decreases in basal ganglia, starting from the caudate (P < 0.0001) and extending to the putamen and globus pallidus (P ≤ 0.001). The longer the disease duration and the higher the CAG repeats, the higher the iron accumulation and the smaller the volume. In the cortex, iron decreases in parieto-occipital areas in SS (P < 0.027); extending to premotor and parieto-temporo-occipital areas in patients (P < 0.003); while volume declines in frontoparietal and temporal areas in presymptomatic (P < 0.023) and SS (P < 0.045), and extends throughout the cortex, with the exception of anterior frontal regions, in patients (P < 0.023). There is an inverse correlation between volume and iron levels in putamen, globus pallidus and the anterior cingulate; and a direct correlation in cortical structures (SMA-sensoriomotor and temporo-occipital). Iron homeostasis is affected in the disease; however, there appear to be differences in the role played by iron in basal ganglia and in cortex.

Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data

Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n>3000). Less than 10% of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen.

The Corticospinal Tract in Huntington's Disease

Huntington's disease (HD) is characterized by progressive motor impairment. Therefore, the connectivity of the corticospinal tract (CST), which is the main white matter (WM) pathway that conducts motor impulses from the primary motor cortex to the spinal cord, merits particular attention. WM abnormalities have already been shown in presymptomatic (Pre-HD) and symptomatic HD subjects using magnetic resonance imaging (MRI). In the present study, we examined CST microstructure using diffusion tensor imaging (DTI)-based tractography in 30-direction DTI data collected from 100 subjects: Pre-HD subjects (n = 25), HD patients (n = 25) and control subjects (n = 50), and T2*-weighted (iron sensitive) imaging. Results show decreased fractional anisotropy (FA) and increased axial (AD), and radial diffusivity (RD) in the bilateral CST of HD patients. Pre-HD subjects had elevated iron in the left CST, regionally localized between the brainstem and thalamus. CAG repeat length in conjunction with age, as well as motor (UHDRS) assessment were correlated with CST FA, AD, and RD both in Pre-HD and HD. In the presymptomatic phase, increased iron in the inferior portion supports the "dying back" hypothesis that axonal damage advances in a retrograde fashion. Furthermore, early iron alteration may cause a high level of toxicity, which may contribute to further damage.

A two years longitudinal study of a transgenic Huntington disease monkey

Background

A two-year longitudinal study composed of morphometric MRI measures and cognitive behavioral evaluation was performed on a transgenic Huntington’s disease (HD) monkey. rHD1, a transgenic HD monkey expressing exon 1 of the human gene encoding huntingtin (HTT) with 29 CAG repeats regulated by a human polyubiquitin C promoter was used together with four age-matched wild-type control monkeys. This is the first study on a primate model of human HD based on longitudinal clinical measurements.

Results

Changes in striatal and hippocampal volumes in rHD1 were observed with progressive impairment in motor functions and cognitive decline, including deficits in learning stimulus-reward associations, recognition memory and spatial memory. The results demonstrate a progressive cognitive decline and morphometric changes in the striatum and hippocampus in a transgenic HD monkey.

Conclusions

This is the first study on a primate model of human HD based on longitudinal clinical measurements. While this study is based a single HD monkey, an ongoing longitudinal study with additional HD monkeys will be important for the confirmation of our findings. A nonhuman primate model of HD could complement other animal models of HD to better understand the pathogenesis of HD and future development of diagnostics and therapeutics through longitudinal assessment.

Diffeomorphic shape trajectories for improved longitudinal segmentation and statistics

Longitudinal imaging studies involve tracking changes in individuals by repeated image acquisition over time. The goal of these studies is to quantify biological shape variability within and across individuals, and also to distinguish between normal and disease populations. However, data variability is influenced by outside sources such as image acquisition, image calibration, human expert judgment, and limited robustness of segmentation and registration algorithms. In this paper, we propose a two-stage method for the statistical analysis of longitudinal shape. In the first stage, we estimate diffeomorphic shape trajectories for each individual that minimize inconsistencies in segmented shapes across time. This is followed by a longitudinal mixed-effects statistical model in the second stage for testing differences in shape trajectories between groups. We apply our method to a longitudinal database from PREDICT-HD and demonstrate our approach reduces unwanted variability for both shape and derived measures, such as volume. This leads to greater statistical power to distinguish differences in shape trajectory between healthy subjects and subjects with a genetic biomarker for Huntington's disease (HD).

Prefrontal executive function associated coupling relates to Huntington's disease stage

Huntington's disease (HD) is a neurodegenerative disease caused by cytosine-adenine-guanine (CAG)-repeat expansion in the huntingtin (HTT) gene. Early changes that may precede clinical manifestation of movement disorder include executive dysfunction. The aim of this study was to identify functional network correlates of impaired higher cognitive functioning in relation to HD stage. Blood-oxygenation-level-dependent (BOLD) functional-magnetic resonance imaging (fMRI) and structural-MRI were performed in 53 subjects with the HD-mutation (41 prodromals, 12 early affected) and 52 controls. Disease stage was estimated for each subject with HD-mutation based on age, length of the CAG-repeat expansion mutation and also putaminal atrophy. The Tower of London test was administered with three levels of complexity during fMRI as a challenge of executive function. Functional brain networks of interest were identified based on cortical gray matter voxel-clusters with significantly enhanced task-related functional coupling to the medial prefrontal cortex (MPFC) area. While prodromal HD-subjects showed similar performance levels as controls, multivariate analysis of task-related functional coupling to the MPFC identified reduced connectivity in prodromal and early manifest HD-subjects for a cluster including mainly parts of the left premotor area. Secondary testing indicated a significant moderator effect for task complexity on group differences and on the degree of correlation to measures of HD stage. Our data suggest that impaired premotor-MPFC coupling reflects HD stage related dysfunction of cognitive systems involved in executive function and may be present in prodromal HD-subjects that are still cognitively normal. Additional longitudinal studies may reveal temporal relationships between impaired task-related premotor-MPFC coupling and other brain changes in HD.

Striatal morphology as a biomarker in neurodegenerative disease

The striatum, comprising the caudate nucleus, putamen and nucleus accumbens, occupies a strategic location within cortico-striato-pallido-thalamic-cortical (corticostriatal) re-entrant neural circuits. Striatal neurodevelopment is precisely determined by phylogenetically conserved homeobox genes. Consisting primarily of medium spiny neurons, the striatum is strictly topographically organized based on cortical afferents and efferents. Particular corticostriatal neural circuits are considered to subserve certain domains of cognition, emotion and behaviour. Thus, the striatum may serve as a map of structural change in the cortical afferent pathways owing to deafferentation or neuroplasticity, and conversely, structural change in the striatum per se may structurally disrupt corticostriatal pathways. The morphology of the striatum may be quantified in vivo using advanced magnetic resonance imaging, as may cognitive functioning pertaining to corticostriatal circuits. It is proposed that striatal morphology may be a biomarker in neurodegenerative disease and potentially the basis of an endophenotype.