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. 2017 Jul 26;2017:92-102.
eCollection 2017.

An Exploration of Latent Structure in Observational Huntington's Disease Studies

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Free PMC article

An Exploration of Latent Structure in Observational Huntington's Disease Studies

Soumya Ghosh et al. AMIA Jt Summits Transl Sci Proc. .
Free PMC article

Abstract

Huntington's disease (HD) is a monogenic neurodegenerative disorder characterized by the progressive decay of motor and cognitive abilities accompanied by psychiatric episodes. Tracking and modeling the progression of the multi-faceted clinical symptoms of HD is a challenging problem that has important implications for staging of HD patients and the development of improved enrollment criteria for future HD studies and trials. In this paper, we describe the first steps towards this goal. We begin by curating data from four recent observational HD studies, each containing a diverse collection of clinical assessments. The resulting dataset is unprecedented in size and contains data from 19,269 study participants. By analyzing this large dataset, we are able to discover hidden low dimensional structure in the data that correlates well with surrogate measures of HD progression. The discovered structures are promising candidates for future consumption by downstream statistical HD progression models.

Figures

Figure 1.
Figure 1.
Descriptive statistics summarizing the aggregated HD dataset.
Figure 2.
Figure 2.
Graphical model depicting the conditional dependencies assumed by the model. Shaded nodes indicate observed random variables. Plates indicate replication and arrows encode conditional dependencies. Right: Heavy tailed behavior of a zero mean, unit variance Student-t distribution for different degrees of freedom ν. The tails get heavier with decreasing ν.
Figure 3.
Figure 3.
Hinton diagrams of posterior means (E[W | x]) of the loading matrices discovered by the model for the different domains. White squares indicate positive values and black indicates negative values. Larger squares indicate larger magnitude. Within each domain the columns of the matrix are sorted from left to right according to the proportion of variance explained by the column. The leftmost column corresponds to the direction of maximum variance within that domain.
Figure 4.
Figure 4.
Correlation of the dominant direction of variation in the motor, functional, cognitive and behavioral domains with CAP and coarse HD stages. The x axis corresponds to the posterior means (E[zn1 | x]) of projections of different observations along the dominant direction of variance (the leftmost column of the matrices displayed in Figure 4), the y axis represents the CAP score associated with the observation. The colors correspond to the coarse HD stage of the observation. In all but the behavior domain, the projections correlate well with CAP and HD stages. In the motor domain, increasing projections along the dominant variance detection indicate increasing motor impairment and thus correlate positively with CAP scores. In functional and behavioral domains, increasing scores increase decreasing impairment and thus correlate negatively with CAP.

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