Cognitive Control, Learning, and Clinical Motor Ratings Are Most Highly Associated with Basal Ganglia Brain Volumes in the Premanifest Huntington's Disease Phenotype

Abstract

Objectives: Huntington's disease (HD) is a debilitating genetic disorder characterized by motor, cognitive and psychiatric abnormalities associated with neuropathological decline. HD pathology is the result of an extended chain of CAG (cytosine, adenine, guanine) trinucleotide repetitions in the HTT gene. Clinical diagnosis of HD requires the presence of an otherwise unexplained extrapyramidal movement disorder in a participant at risk for HD. Over the past 15 years, evidence has shown that cognitive, psychiatric, and subtle motor dysfunction is evident decades before traditional motor diagnosis. This study examines the relationships among subcortical brain volumes and measures of emerging disease phenotype in prodromal HD, before clinical diagnosis.

Methods: The dataset includes 34 cognitive, motor, psychiatric, and functional variables and five subcortical brain volumes from 984 prodromal HD individuals enrolled in the PREDICT HD study. Using cluster analyses, seven distinct clusters encompassing cognitive, motor, psychiatric, and functional domains were identified. Individual cluster scores were then regressed against the subcortical brain volumetric measurements.

Results: Accounting for site and genetic burden (the interaction of age and CAG repeat length) smaller caudate and putamen volumes were related to clusters reflecting motor symptom severity, cognitive control, and verbal learning.

Conclusions: Variable reduction of the HD phenotype using cluster analysis revealed biologically related domains of HD and are suitable for future research with this population. Our cognitive control cluster scores show sensitivity to changes in basal ganglia both within and outside the striatum that may not be captured by examining only motor scores. (JINS, 2017, 23, 159-170).

Keywords: Basal ganglia; Caudate; Cluster analysis; Clustering; Cognition; Cognitive control; Neuropsychology; Prodromal Huntington’s disease; Psychiatric symptoms; Striatum.

Figure 1

Results from the hierarchical cluster analysis. Interpretation of the clusters is conducted using the cut-off point as well as the distance shown between each classification dissection. For instance the first three subdivisions are more robust than the next four using distance parameters legend: Blue line depicts our cut point, and red line indicates our cluster score groups. Bold lettering on the left depicts the name of the cluster, and in smaller text are the names of individual measures used to calculate each cluster score.

Figure 2

Motor Symptom cluster scores and caudate volumes(B=−1, t(936)= −2.74, p=.02) Legend: Blue line indicates regression line of best fit, grey dots are data points.

Figure 3

Motor Symptom cluster scores and putamen volumes (B=−1.07, t(936)= −2.36, p=.04) Legend: Blue line indicates regression line of best fit, grey dots are data points.

Figure 4

Cognitive control cluster scores and Globus Pallidus volumes. (B=1.42, t(904)= 1.17, p=.03). Legend: Blue line indicates regression line of best fit, grey dots are data points.

Figure 5

Cognitive control cluster scores and putamen volumes. (B=1.31, t(904)= 2.59, p=.02) Legend: Blue line indicates regression line of best fit, grey dots are data points.

Figure 6

Cognitive control cluster scores and nucleus accumbens volumes. (B=1.22, t(904)= 2.52, p=.02) Legend: Blue line indicates regression line of best fit, grey dots are data points.

Figure 7

Verbal Learning subcluster scores and caudate volumes. (B= 1.99, t(664)= 3.07, p=.005) Clustering and factor analysis are often used for the same purpose: to find homogenous subset of variables, i.e., to find clusters. Clustering has the advantage in my mind of forcing variables into unambiguous clusters, whereas factor analysis does not do so and a variable can have similar loadings on many factors. Ji-in did a thorough job in investigating the variables at baseline, but we never published a paper. I think this would be great for you to do for a paper, at least as a preliminary analysis step. Legend: Blue line indicates regression line of best fit, grey dots are data points.