Onset and rate of striatal atrophy in preclinical Huntington disease

Aylward, Elizabeth H.; Sparks, Bobbi F.; Field, Katharine G.; Yallapragada, V.; Shpritz, Barnett; Rosenblatt, Adam; Brandt, Jason; Gourley, Lisa M.; Liang, Kung Yee; Zhou, Hufang; Margolis, Russell L.; Ross, Christopher A.

doi:10.1212/01.wnl.0000132965.14653.d1

Cited by 365 publications

(326 citation statements)

References 22 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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“…The early involvement of the striatum is well reported 28, 29, 30. Early changes in the basal ganglia nuclei – here represented by the putamen, caudate, and pallidum – are in agreement with previous observations in both premanifest and manifest HD subjects using the TRACK‐HD dataset,24 and with other studies based on the putamen and caudate 29, 30. Their ordering, however, has not previously been observed: the EBM places the putamen strongly ahead of the caudate and pallidum, even under bootstrapping.…”

Section: Discussionmentioning

confidence: 90%

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

Wijeratne

Young

Oxtoby

et al. 2018

Ann Clin Transl Neurol

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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.

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“…Images were acquired on a 1.5T GE Genesis Signa scanner using a SPGR sequence (TR=18ms,TE=3ms,N=2,flip angle=20°) with an axial orientation, image dimensions 256×256×124 and voxel dimensions 0.9375×0.9375×1.5 mm. The left and right caudate nucleus and putamen were manually outlined following the protocol used by Aylward et al (2004). Note that the manual segmentation protocol used for this dataset differs from that of FreeSurfer.…”

Section: Methodsmentioning

confidence: 99%

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

Wang²,

2008

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Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM) based label propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse to fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM).One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail.We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0. 81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS +LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.

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Onset and rate of striatal atrophy in preclinical Huntington disease

Abstract: Striatal atrophy begins many years prior to diagnosable HD, and assessment of atrophy on MRI may be very useful in both predicting HD onset and in tracking progression in future therapeutic trials in preclinical subjects.

Cited by 365 publications

References 22 publications

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

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

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

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

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