Brain Structure in Preclinical Huntington’s Disease

Paulsen, Jane S.; Magnotta, Vincent A.; Mikos, Ania; Paulson, Henry L.; Penziner, Elizabeth; Andreasen, Nancy C.; Nopoulos, Peg

doi:10.1016/j.biopsych.2005.06.003

Cited by 216 publications

(230 citation statements)

References 44 publications

Supporting

Mentioning

196

Contrasting

Unclassified

Order By: Relevance

“…Their ordering, however, has not previously been observed: the EBM places the putamen strongly ahead of the caudate and pallidum, even under bootstrapping. Abnormalities in the insula white matter and nonventricular CSF are identified by the EBM as potential mid‐stage biomarkers, the former of which is also reported in the PREDICT‐HD dataset,27 and agrees with observations of white matter abnormality in premanifest and manifest subjects 31, 32, 33, 34…”

Section: Discussionsupporting

confidence: 81%

An image‐based model of brain volume biomarker changes in Huntington's disease

Wijeratne

Young

Oxtoby

et al. 2018

Ann Clin Transl Neurol

View full text Add to dashboard Cite

ObjectiveDetermining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.MethodsWe employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.ResultsThe model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.InterpretationWe used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.

show abstract

Section: Discussionsupporting

confidence: 81%

An image‐based model of brain volume biomarker changes in Huntington's disease

Wijeratne

Young

Oxtoby

et al. 2018

Ann Clin Transl Neurol

View full text Add to dashboard Cite

show abstract

“…15,16 However, few VBM studies of neurodegeneration include an index or measure of TIV as a covariate, though many adjust for total GM volume. In healthy subjects, total GM volume is likely to correlate with TIV, though it will decrease with age.…”

Section: Adjusting For Brain Volumementioning

confidence: 99%

Pitfalls in the Use of Voxel-Based Morphometry as a Biomarker: Examples from Huntington Disease

Henley¹,

Ridgway²,

Scahill³

et al. 2009

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

“…In addition, cortical white matter decreases as the estimated years to disease onset decreases (Paulsen, et al, 2006;Stoffers, et al, 2010). However, cortical gray matter is above normal in pHD with large estimated years to onset and decreases to below normal the nearer the estimated onset time (Paulsen, et al, 2006; also observable in Stoffers et al, 2010). This increased gray matter volume is consistent with the increased functional activation in medial frontal areas (Beste, et al, 2007;Paulsen, et al, 2004), which is interpreted as reflecting a compensatory neurodevelopmental mechanism.…”

Section: Compensatory Mechanismsmentioning

confidence: 85%