Basal Ganglia Volume and Proximity to Onset in Presymptomatic Huntington Disease

Aylward, Elizabeth H.; Codori, Ann Marie; Barta, Patrick E.; Pearlson, Godfrey D.; Harris, Gordon J.; Brandt, Jason

doi:10.1001/archneur.1996.00550120105023

Cited by 179 publications

(163 citation statements)

References 7 publications

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“…Given the resilience of these findings, which have been demonstrated consistently across multiple studies in preHD,22, 23, 24, 25, 26, 27, 28, 29 we would suggest that analyses of subcortical volume becomes a minimum requirement for any future preclinical disease modifying trials in HD. Notably, structural volumetric analyses are likely to be more robust across scanners than resting‐state functional neuroimaging measures; therefore, they are also more likely to form a tractable basis for a standardized polymarker that can be used to integrate findings across studies and sites.…”

Section: Discussionmentioning

confidence: 73%

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

Mason

Daws

Soreq

et al. 2018

Annals of Neurology

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ObjectiveHuntington'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.MethodA 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.ResultsClassification 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.InterpretationWe 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

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Section: Discussionmentioning

confidence: 73%

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

Mason

Daws

Soreq

et al. 2018

Annals of Neurology

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“…Even though striatal atrophy has been detected and reported by many investigators (3,(25)(26)(27)(28)(29), neuronal degeneration also occurs in other areas of the brain such as the cerebral and cerebellar cortex (4,5), amygdala, hippocampus, and brainstem (6), supporting the concept that HD is a multisystem disorder. In agreement with this concept, subcortical gray matter loss in the thalamus, substantia nigra, and anterior basomedial diencephalic region has been described, but in later stages of the disease (5).…”

Section: Discussionmentioning

confidence: 92%

Striatal and extrastriatal atrophy in Huntington's disease and its relationship with length of the CAG repeat

Ruocco

Lopes‐Cendes

et al. 2006

Braz J Med Biol Res

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Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that affects the striatum most severely. However, except for juvenile forms, relative preservation of the cerebellum has been reported. The objective of the present study was to perform MRI measurements of caudate, putamen, cerebral, and cerebellar volumes and correlate these findings with the length of the CAG repeat and clinical parameters. We evaluated 50 consecutive patients with HD using MRI volumetric measurements and compared them to normal controls. Age at onset of the disease ranged from 4 to 73 years (mean: 43.1 years). The length of the CAG repeat ranged from 40 to 69 (mean: 47.2 CAG). HD patients presented marked atrophy of the caudate and putamen, as well as reduced cerebellar and cerebral volumes. There was a significant correlation between age at onset of HD and length of the CAG repeat, as well as clinical disability and age at onset. The degree of basal ganglia atrophy correlated with the length of the CAG repeat. There was no correlation between cerebellar or cerebral volume and length of the CAG repeat. However, there was a tendency to a positive correlation between duration of disease and cerebellar atrophy. While there was a negative correlation of length of the CAG repeat with age at disease onset and with striatal degeneration, its influence on extrastriatal atrophy, including the cerebellum, was not clear. Extrastriatal atrophy occurs later in HD and may be related to disease duration.

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“…), was blinded to participant characteristics and completed all measurements for the study. Measurements were made by manually drawing boundaries of the caudate (head and body) and putamen, as previously described (Aylward et al, 1996;Aylward et al, 1997), and volumes were calculated based on the number of identified pixels. Caudate and putamen volumes were summed to obtain a measure of total striatal volume.…”

Section: Participant Characterizationmentioning

confidence: 99%

“…Several studies have documented structural and functional brain changes in CAG-expanded individuals who have not yet been diagnosed (Aylward et al, 1996;Aylward et al, 2000;Aylward et al, 2004;Kipps et al, 2005;Paulsen et al, 2006b;Reading et al, 2004). Although relatively few studies have investigated the association between cognition and brain changes in HD, a recent study showed that cognitive task performance correlated with cerebral white matter and striatal volumes in early HD (Beglinger et al, 2005).…”

Section: Introductionmentioning

confidence: 99%