Estimating parameters in a mixture of normal distributions dates back to the 19th century when Pearson originally considered data of crabs from the Bay of Naples. Since then, many real world applications of mixtures have led to various proposed methods for studying similar problems. Among them, maximum likelihood estimation (MLE) and the continuous empirical characteristic function (CECF) methods have drawn the most attention. However, the performance of these competing estimation methods has not been thoroughly studied in the literature and conclusions have not been consistent in published research. In this article, we review this classical problem with a focus on estimation bias. An extensive simulation study is conducted to compare the estimation bias between the MLE and CECF methods over a wide range of disparity values. We use the overlapping coefficient (OVL) to measure the amount of disparity, and provide a practical guideline for estimation quality in mixtures of normal distributions. Application to an ongoing multi-site Huntington disease study is illustrated for ascertaining cognitive biomarkers of disease progression.
Biomarkers; Disparity index; EM algorithm; Mixtures
The success of clinical trials in Huntington disease (HD) will depend to a large degree on the quality of the outcome measures. Using data from the TRACK-HD study, a recent publication proposes a battery of assessments that could be used as outcomes in future clinical trials in patients with early HD.
The past decade has witnessed an explosion of evidence suggesting that many neurodegenerative diseases can be detected years, if not decades, earlier than previously thought. To date, these scientific advances have not provoked any parallel translational or clinical improvements. There is an urgency to capitalize on this momentum so earlier detection of disease can be more readily translated into improved health-related quality of life for families at risk for, or suffering with, neurodegenerative diseases. In this review, we discuss health-related quality of life (HRQOL) measurement in neurodegenerative diseases and the importance of these “patient reported outcomes” for all clinical research. Next, we address HRQOL following early identification or predictive genetic testing in some neurodegenerative diseases: Huntington disease, Alzheimer's disease, Parkinson's disease, Dementia with Lewy bodies, frontotemporal dementia, amyotrophic lateral sclerosis, prion diseases, hereditary ataxias, Dentatorubral-pallidoluysian atrophy and Wilson's disease. After a brief report of available direct-to-consumer genetic tests, we address the juxtaposition of earlier disease identification with assumed reluctance towards predictive genetic testing. Forty-one studies examining health related outcomes following predictive genetic testing for neurodegenerative disease suggested that (a) extreme or catastrophic outcomes are rare; (b) consequences commonly include transiently increased anxiety and/or depression; (c) most participants report no regret; (d) many persons report extensive benefits to receiving genetic information; and (e) stigmatization and discrimination for genetic diseases are poorly understood and policy and laws are needed. Caution is appropriate for earlier identification of neurodegenerative diseases but findings suggest further progress is safe, feasible and likely to advance clinical care.
Controversy exists regarding the feasibility of preventive clinical trials in prodromal Huntington disease (HD). A primary limitation is a lack of outcome measures for persons with the gene mutation who have not yet been diagnosed with HD. Many longitudinal studies of cognitive decline in prodromal HD have not stratified samples based on disease progression, thereby obscuring differences between symptomatic and nonsymptomatic individuals. Prodromal participants from PREDICT-HD were stratified by disease progression into one of three groups: those having a High, Medium, or Low probability of motor manifestation within the next five years. Data from a total of N = 1299 participants with up to 5950 data points were subjected to linear mixed effects regression on 29 longitudinal cognitive variables, controlling for age, education, depression, and gender. Performance of the three prodromal HD groups was characterized by insidious and significant cognitive decline over time. Twenty-one variables from 19 distinct cognitive tasks revealed evidence of a disease progression gradient, meaning that the rate of deterioration varied as a function of progression level, with faster deterioration associated with greater disease progression. Nineteen measures showed significant longitudinal change in the High group, nine showed significant change in the Medium group and four showed significant cognitive decline in the Low group. Results indicate that clinical trials may be conducted in prodromal HD using the outcome measures and methods specified. The findings may help inform interventions in HD as well as other neurodegenerative disorders.
Depression causes significant morbidity and mortality, and this also occurs in Huntington Disease (HD), an inherited neurodegenerative illness with motor, cognitive, and psychiatric symptoms. The presentation of depression in this population remains poorly understood, particularly in the prodromal period before development of significant motor symptoms. In this study, we assessed depressive symptoms in a sample of 803 individuals with the HD mutation in the prodromal stage and 223 mutation-negative participants at the time of entry in the Neurobiological Predictors of HD (PREDICT-HD) study. Clinical and biological HD variables potentially related to severity of depression were analyzed. A factor analysis was conducted to characterize the symptom domains of depression in a subset (n=168) with clinically significant depressive symptoms. Depressive symptoms were found to be more prevalent in HD mutation carriers but did not increase with proximity to HD diagnosis and were not associated with length of the HD mutation. Increased depressive symptoms were significantly associated with female gender, self-report of past history of depression, and a slight decrease in functioning, but not with time since genetic testing. The factor analysis identified symptom domains similar to prior studies in other populations. These results show that individuals with the HD mutation are at increased risk to develop depressive symptoms at any time during the HD prodrome. The clinical presentation appears to be similar to other populations. Severity and progression are not related to the HD mutation.
Huntington Disease; Depression; Suicide; Genetic testing
To determine whether Huntington disease (HD) mutation carriers have motor symptoms (complaints) when definite motor onset (motor phenoconversion) is diagnosed and document differences between the groups with and without unawareness of motor signs.
We analyzed data from 550 HD mutation carriers participating in the multicenter PREDICT-HD Study followed through the HD prodrome. Data analysis included demographics, the Unified Huntington’s Disease Rating Scale (UHDRS) and the Participant HD History of symptoms, self-report of progression, and cognitive, behavioral, and imaging measures. Unawareness was identified when no motor symptoms were self-reported but when definite motor HD was diagnosed.
Of 38 (6.91%) with onset of motor HD, almost half (18/38 = 47.36%) had no motor symptoms despite signs of disease on the UHDRS motor rating and consistent with unawareness. A group with motor symptoms and signs was similar on a range of measures to the unaware group. Those with unawareness of HD signs reported less depression. Patients with symptoms had more striatal atrophy on imaging measures.
Only half of the patients with newly diagnosed motor HD had motor symptoms. Unaware patients were less likely to be depressed. Self-report of symptoms may be inaccurate in HD at the earliest stage.
Huntington disease is a neurodegenerative disorder that involves preferential atrophy in the striatal complex and related subcortical nuclei. In this paper, which is based on a dataset extracted from the PREDICT-HD study, we use statistical shape analysis with deformation markers obtained through Large Deformation Diffeomorphic Metric Mapping of cortical surfaces to highlight specific atrophy patterns in the caudate, putamen, and globus pallidus, at different prodromal stages of the disease. Based on the relation to cortico-basal-ganglia circuitry, we propose that statistical shape analysis, along with other structural and functional imaging studies, may help expand our understanding of the brain circuitry affected and other aspects of the neurobiology of HD, and also guide the most effective strategies for intervention.
striatal atrophy; pallidus atrophy; diffeomorphic mapping; surface registration; surface-based morphometry
Huntington disease (HD) is associated with decline in cognition and progressive morphological changes in brain structures. Cognitive reserve may represent a mechanism by which disease-related decline may be delayed or slowed. The current study examined the relationship between cognitive reserve and longitudinal change in cognitive functioning and brain volumes among prodromal (gene expansion-positive) HD individuals.
Participants were genetically-confirmed individuals with prodromal HD enrolled in the PREDICT-HD study. Cognitive reserve was computed as the composite of performance on a lexical task estimating premorbid intellectual level, occupational status, and years of education. Linear mixed effects regression (LMER) was used to examine longitudinal changes on 4 cognitive measures and 3 brain volumes over approximately 6 years.
Higher cognitive reserve was significantly associated with a slower rate of change on one cognitive measure (Trail Making Test, Part B) and slower rate of volume loss in two brain structures (caudate, putamen) for those estimated to be closest to motor disease onset. This relationship was not observed among those estimated to be further from motor disease onset.
Our findings demonstrate a relationship between cognitive reserve and both a measure of executive functioning and integrity of certain brain structures in prodromal HD individuals.
Huntington disease; prodromal; cognitive reserve; cognition; caudate; putamen
24S-hydroxycholesterol (24OHC) is involved in the conversion of excess cholesterol in the brain, and its level in plasma is related to the number of metabolically active neuronal cells. Previous research suggests that plasma 24OHC is substantially reduced in the presence of neurodegenerative disease. Huntington disease (HD) is an inherited autosomal dominant neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) triplet repeat expansion in the coding region of the huntingtin (HTT) gene. The current study focused on the relative importance of 24OHC as a marker of HD progression. Using mass spectrometry methods, plasma 24OHC levels were examined in three groups of gene-expanded individuals (Low, Medium, High) characterized by their progression at entry into the parent PREDICT-HD study, along with a group of non-gene-expanded controls (total N = 150). In addition, the correlation of 24OHC with a number of motor, cognitive, and imagining markers was examined, and effect sizes for group differences among the markers were computed for comparison with 24OHC. Results show a progression gradient as 24OHC levels decreased as the progression group increased (Low to High). The effect size of group differences for 24OHC was larger than all the other variables, except striatal volume. 24OHC was significantly correlated with many of the other key variables. The results are interpreted in terms of cholesterol synthesis and neuronal degeneration. This study provides evidence that 24OHC is a relatively important marker of HD progression.
Using a novel quantitative model of repeated choice behavior, we investigated the cognitive processes of criminal offenders incarcerated for various crimes. Eighty-one criminals, including violent offenders, drug and sex offenders, drivers operating a vehicle while impaired (OWI) and eighteen matched controls were tested. The results were also contrasted to those obtained from neurological patients with focal brain lesions in the orbitofrontal cortex, and from drug abusers. Participants performed the computerized version of the Iowa Gambling Task (Bechara et al., 1994), and the results were decomposed into specific component processes using the Expectancy Valence model (Busemeyer & Stout, 2002). The findings indicated that whereas all criminal groups tended to select disadvantageously, the cognitive profiles exhibited by different groups were considerably different. Certain subpopulations, most significantly drug and sex offenders, overweighted potential gains compared to losses, similar to chronic cocaine abusers. In contrast, assault/murder criminals tended to make less consistent choices and to focus on immediate outcomes, and in these respects were more similar to patients with orbitofrontal damage. The current cognitive model provides a novel way for building a bridge between cognitive neuroscience and complex human behaviors.
decision making; criminal; cognitive modeling; individual differences; learning; impulsivity
Huntington’s disease (HD) is a well-recognized progressive neurodegenerative disorder that follows an autosomal dominant pattern of inheritance. Onset is insidious and can occur at almost any age, but most commonly the diagnosis is made between the ages of 35 and 55 years. Onset ≤20 years of age is classified as juvenile HD (JHD). This age-based definition is arbitrary but remains convenient. There is overlap between the clinical pathological and genetic features seen in JHD and more traditional adult-onset HD. Nonetheless, the frequent predominance of bradykinesia and dystonia early in the course of the illness, more frequent occurrence of epilepsy and myoclonus, more widespread pathology, and larger genetic lesion means that the distinction is still relevant. In addition, the relative rarity of JHD means that the clinician managing the patient is often doing so for the first time. Management is, at best, symptomatic and supportive with few or no evidence-based guidelines. In this article, the authors will review what is known of the condition and present some suggestions based on their experience.
Executive dysfunction (ED) is a characteristic of Huntington disease (HD), but its severity and progression is less understood in the prodromal phase, e.g., before gross motor abnormalities. We examined planning and problem-solving abilities using the Towers Task in HD mutation-positive individuals without motor symptoms (n = 781) and controls (n = 212). Participants with greater disease progression (determined using mutation size and current age) performed more slowly and with less accuracy on the Towers Task. Performance accuracy was negatively related to striatal volume while both accuracy and working memory were negatively related to frontal white matter volume. Disease progression at baseline was not associated with longitudinal performance over 4 years. Whereas the baseline findings indicate that ED becomes more prevalent with greater disease progression in prodromal HD and can be quantified using the Towers task, the absence of notable longitudinal findings indicates that the Towers Task exhibits limited sensitivity to cognitive decline in this population.
Huntington's disease; Genetic disorders; Executive functions; Neuroimaging (structural); Norms/normative studies; Practice effects/reliable change; longitudinal change
Aims: This study examines elements of genetic discrimination among an at-risk, clinically undiagnosed Huntington's disease (HD) population. Methods: Sixty at-risk individuals, either positive or negative for the HD genetic mutation, completed a survey regarding their experiences of genetic discrimination, adverse and unfair treatment, and knowledge about existing laws and policies surrounding genetic discrimination. Results: Sixty eight percent of participants reported feeling “Great benefit” from knowing their genetic test results. Reported benefits of knowledge included planning for the future, making decisions, and many individuals found meaning in active participation in the HD community and in advocating for themselves or families at risk for HD. Many individuals found personal meaning and a sense of community from knowledge of this information and from the ability to participate in research. Despite these positive feelings toward gene testing, results demonstrated that 33% of participants perceived experiences of genetic discrimination, which occurred repeatedly and caused great self-reported distress. Significantly, more gene-positive respondents reported experiencing incidents of genetic discrimination, compared to gene-negative respondents. At least 58 separate incidents of discrimination were reported, the number of incidents ranged from 1 to 10, with 45% of individuals (9/20 respondents) indicating more than one event. Of the most significant events of discrimination, 58% were related to insurance, 21% to employment, 16% to transactions of daily life, and 5% to relationships. Conclusion: Results contribute toward validation of empirical data regarding genetic discrimination.
The brain mechanisms of cognitive impairment in prodromal Huntington disease (prHD) are not well understood. Although striatal atrophy correlates with some cognitive abilities, few studies of prHD have investigated whether cortical gray matter morphometry correlates in a regionally specific manner with functioning in different cognitive domains. This knowledge would inform the selection of cognitive measures for clinical trials that would be most sensitive to the target of a treatment intervention.
In this study, random forest analysis was used to identify neuroanatomical correlates of functioning in five cognitive domains including attention and information processing speed, working memory, verbal learning and memory, negative emotion recognition, and temporal processing. Participants included 325 prHD individuals with varying levels of disease progression and 119 gene-negative controls with a family history of HD. In intermediate analyses, we identified brain regions that showed significant differences between the prHD and the control groups in cortical thickness and striatal volume. Brain morphometry in these regions was then correlated with cognitive functioning in each of the domains in the prHD group using random forest methods. We hypothesized that different regional patterns of brain morphometry would be associated with performances in distinct cognitive domains.
The results showed that performances in different cognitive domains that are vulnerable to decline in prHD were correlated with regionally specific patterns of cortical and striatal morphometry. Putamen and/or caudate volumes were top-ranked correlates of performance across all cognitive domains, as was cortical thickness in regions related to the processing demands of each domain.
The results underscore the importance of identifying structural magnetic resonance imaging (sMRI) markers of functioning in different cognitive domains, as their relative sensitivity depends on the extent to which processing is called upon by different brain networks. The findings have implications for identifying neuroimaging and cognitive outcome measures for use in clinical trials.
Cognition; magnetic resonance imaging; prodromal Huntington disease
There is growing consensus that intervention and treatment of Huntington disease (HD) should occur at the earliest stage possible. Various early-intervention methods for this fatal neurodegenerative disease have been identified, but preventive clinical trials for HD are limited by a lack of knowledge of the natural history of the disease and a dearth of appropriate outcome measures. Objectives of the current study are to document the natural history of premanifest HD progression in the largest cohort ever studied and to develop a battery of imaging and clinical markers of premanifest HD progression that can be used as outcome measures in preventive clinical trials. Neurobiological predictors of Huntington’s disease is a 32-site, international, observational study of premanifest HD, with annual examination of 1013 participants with premanifest HD and 301 gene-expansion negative controls between 2001 and 2012. Findings document 39 variables representing imaging, motor, cognitive, functional, and psychiatric domains, showing different rates of decline between premanifest HD and controls. Required sample size and models of premanifest HD are presented to inform future design of clinical and preclinical research. Preventive clinical trials in premanifest HD with participants who have a medium or high probability of motor onset are calculated to be as resource-effective as those conducted in diagnosed HD and could interrupt disease 7–12 years earlier. Methods and measures for preventive clinical trials in premanifest HD more than a dozen years from motor onset are also feasible. These findings represent the most thorough documentation of a clinical battery for experimental therapeutics in stages of premanifest HD, the time period for which effective intervention may provide the most positive possible outcome for patients and their families affected by this devastating disease.
Huntington disease; neurodegenerative disorders; premanifest; natural history; clinical trials; outcome measures; PREDICT-HD
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
MRI; fMRI; intrinsic networks; classification; unsupervised learning
In volumetric brain imaging analysis, volumes of brain structures are typically assumed to be proportional or linearly related to intracranial volume (ICV). However, evidence abounds that many brain structures have power law relationships with ICV. To take this relationship into account in volumetric imaging analysis, we propose a power law based method—the power-proportion method—for ICV correction. The performance of the new method is demonstrated using data from the PREDICT-HD study.
magnetic resonance imaging; intracranial volume; nonlinear model; power function; power-proportion correction
The purpose of this study was to identify factors that are associated with experiencing genetic discrimination (GD) among individuals at risk for Huntington disease (HD). Multivariable logistic regression analysis was used to examine factors associated with experiencing GD in data from a cross-sectional, self-report survey of 293 individuals at risk for HD. The study sample comprised 167 genetically tested respondents, and 66 who were not tested (80% response rate). Overall, individuals who learn they are at risk for HD at a younger age (OR = 3.1; 95% CI: 1.5–6.2; P = 0.002), are mutation-positive (OR = 2.8; 95% CI: 1.4–6.0; P = 0.006), or are highly educated (OR = 2.7; 95% CI: 1.4–5.1; P = 0.002) are more likely to experience GD, particularly in insurance, family, and social settings. Further, younger age was associated with discrimination in insurance (OR = 0.97; 95% CI: 0.94–1.00; P = 0.038). This study provides evidence that some people who are at risk for HD were more likely to experience GD than others. Individuals who learned they are at risk for HD at a younger age and those who are mutation-positive were more likely to experience GD, particularly in insurance, family, and social settings. Younger individuals were more likely to experience discrimination in the insurance setting. Overall, highly educated individuals were also more likely to report discrimination. These results provide direction for clinical and family discussions, counseling practice, and policy aimed at mitigating experiences of GD.
genetic discrimination; Huntington disease; genetic testing; socio-demographic factors
Huntington disease (HD) is a devastating illness, although its autosomal dominant genetic transmission allows a unique opportunity to study apparently healthy individuals before manifest disease. Attempts to study early disease are not unique in neurology (e.g., Mild Cognitive Impairment, Vascular Cognitive Impairment), but studying otherwise-healthy appearing individuals who will go on with nearly 99% certainty to manifest the symptoms of brain disease does provide distinct but valuable information about the true natural history of the disease. The field has witnessed an explosion of research examining possible early indicators of HD during what is now referred to as the “prodrome” of HD. A NIH study in its ninth year (PREDICT-HD) has offered a glimpse into the transition from an apparently healthy state to an obviously diseased state, and can serve as a model for many other genetic diseases, both neurological and non-neurological.
Huntington disease; diagnosis; detection; prevention; biomarkers; clinical endpoints; clinical trials
A number of studies are now collecting diffusion tensor imaging (DTI) data across sites. While the reliability of anatomical images has been established by a number of groups, the reliability of DTI data has not been studied as extensively. In this study, five healthy controls were recruited and imaged at eight imaging centers. Repeated measures were obtained across two imaging protocols allowing intra-subject and inter-site variability to be assessed. Regional measures within white matter were obtained for standard rotationally invariant measures: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. Intra-subject coefficient of variation (CV) was typically <1% for all scalars and regions. Inter-site CV increased to ∼1%–3%. Inter-vendor variation was similar to inter-site variability. This variability includes differences in the actual implementation of the sequence.
diffusion tensor; fractional anisotropy; magnetic resonance; mean diffusivity; reliability; white matter
The BRAINS (Brain Research: Analysis of Images, Networks, and Systems) image analysis software has been in use, and in constant development, for over twenty years. The original neuroimage analysis pipeline using BRAINS was designed as a semi-automated procedure to measure volumes of the cerebral lobes and subcortical structures, requiring manual intervention at several stages in the process. Through use of advanced image processing algorithms the need for manual intervention at stages of image realignment, tissue sampling and mask editing have been eliminated. In addition, inhomogeneity correction, intensity normalization, and mask cleaning routines have been added to improve the accuracy and consistency of the results. The fully automated method, AutoWorkup, is shown in this study to be more reliable (ICC ≥ 0.96, Jaccard index ≥ 0.80 and Dice index ≥ 0.89 for all tissues in all regions) than the average of 18 manual raters. On a set of 1130 good quality scans the failure rate for correct realignment was 1.1%, and manual editing of the brain mask was required on 4% of the scans. In other tests, AutoWorkup is shown to produce measures that are reliable for data acquired across scanners, scanner vendors, and across sequences. Application of AutoWorkup for the analysis of data from the 32-site, multi-vendor PREDICT-HD study yield estimates of reliability to be greater than or equal to 0.90 for all tissues and regions.
BRAINS; Automated image analysis; pipeline; volumetric analysis; morphometry; segmentation
Prodromal Huntington disease (prHD) is associated with a myriad of cognitive changes, but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials.
The present study sought to characterize cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict to time to diagnosis.
Participants included gene-negative and gene-positive prHD participants who were enrolled in the PREDICT-HD study. The CAG/Age Product (CAP) score was the measure of an individual’s genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis.
Six factors were identified: 1) speed/inhibition, 2) verbal working memory, 3) motor planning/speed, 4) attention-information integration, 5) sensory-perceptual processing, and 6) verbal learning/memory. Factor scores were sensitive to a worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms.
The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive HD trials where they may be more sensitive than individual tests.
prodromal Huntington Disease; time to diagnosis; cognition; survival analysis
Huntington’s disease (HD) is a genetic brain disease characterized by loss of capacity in movement control, cognition, and emotional regulation over a period of about 30 years. Since it is well established that clinical impairments and brain atrophy can be detected decades prior to receiving a clinical diagnosis, functional neuroimaging efforts have gained momentum in HD research. In most brain disorders, there is accumulating evidence that the clinical manifestations of disease do not simply depend on the extent of tissue loss, but represent a complex balance among neuronal dysfunction, tissue repair, and circuitry reorganization. Based upon this premise, functional neuroimaging modalities may be more sensitive to the earliest changes in HD than are structural imaging approaches. For this review, PET and fMRI studies conducted in HD samples were summarized. Strengths and limitations of the utilization of functional imaging in HD are discussed and recommendations are offered to facilitate future research endeavors.
Huntington’s disease; Functional imaging; PET; fMRI