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

Wijeratne, P. A.; Young, Alexandra L.; Oxtoby, Neil P.; Marinescu, Rǎzvan V.; Firth, Nicholas C.; Johnson, Eileanoir B.; Mohan, Amrita; Sampaio, Cristina; Scahill, Rachael I.; Tabrizi, Sarah J.

doi:10.1002/acn3.558

Cited by 50 publications

(51 citation statements)

References 47 publications

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“…The detailed formulation and estimation procedure are reviewed in the supplementary materials. The event-based model is a data-driven approach to estimating a longitudinal model of progression using only cross-sectional data that has been applied across multiple neurodegenerative diseases [8,10,11,18,28,31]. Disease progression is described as a probabilistic sequence of events reflecting cumulative abnormality of disease biomarkers.…”

Section: Resultsmentioning

confidence: 99%

“…Longitudinal patterns of disease progression can also be estimated from cross-sectional data by event-based modelling [11]. This method was first applied on patients with familial Alzheimer and Huntington diseases to identify orderings of regional brain atrophy, and, subsequently, to describe progression of other neurological diseases with the use of different types of biomarkers [8,10,18,28,31]. The instrinsic flexibility and the requirement of only cross-sectional data make the event-based model especially suitable for identifying the regional origin and propagation patterns in sCJD subtypes.…”

Section: Introductionmentioning

confidence: 99%

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Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt–Jakob disease

et al. 2020

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Sporadic Creutzfeldt-Jakob disease (sCJD) is a transmissible brain proteinopathy. Five main clinicopathological subtypes (sCJD-MM(V)1,-MM(V)2C,-MV2K,-VV1, and-VV2) are currently distinguished. Histopathological evidence suggests that the localisation of prion aggregates and spongiform lesions varies among subtypes. Establishing whether there is an initial site with detectable imaging abnormalities (epicentre) and an order of lesion propagation would be informative for disease early diagnosis, patient staging, management and recruitment in clinical trials. Diffusion magnetic resonance imaging (MRI) is the most-used and most-sensitive test to detect spongiform degeneration. This study was designed to identify, in vivo and for the first time, subtype-dependent epicentre and lesion propagation in the brain using diffusion-weighted images (DWI), in the largest known cross-sectional dataset of autopsy-proven subjects with sCJD. We estimate lesion propagation by cross-sectional DWI using event-based modelling, a well-established data-driven technique. DWI abnormalities of 594 autopsy-diagnosed subjects (448 patients with sCJD) were scored in 12 brain regions by 1 neuroradiologist blind to the diagnosis. We used the event-based model to reconstruct sequential orderings of lesion propagation in each of five pure subtypes. Follow-up data from 151 patients validated the estimated sequences. Results showed that epicentre and ordering of lesion propagation are subtype specific. The two most common subtypes (-MM1 and-VV2) showed opposite ordering of DWI abnormality appearance: from the neocortex to subcortical regions, and vice versa, respectively. The precuneus was the most likely epicentre also in-MM2 and-VV1 although at variance with-MM1, abnormal signal was also detected early in cingulate and insular cortices. The caudal-rostral sequence of lesion propagation that characterises-VV2 was replicated in-MV2K. Combined, these data-driven models provide unprecedented dynamic insights into subtype-specific epicentre at onset and propagation of the pathologic process, which may also enhance early diagnosis and enable disease staging in sCJD.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt–Jakob disease

et al. 2020

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“…Effect sizes of neuroimaging-based parameters have already been reported for HD patients, i.e., caudate atrophy and ventricular expansion [4,6], and image-based models of brain volume biomarker changes in HD provide insights into HD progression [26]. The focus of the present study was the investigation of longitudinal HD-associated regional atrophy with an unbiased (fully automated and therefore rater-independent) volumetric technique in order to calculate standardized longitudinal effect sizes with the aim of providing sample sizes for future clinical trials.…”

Section: Discussionmentioning

confidence: 99%

Combined cerebral atrophy score in Huntington's disease based on atlas-based MRI volumetry: Sample size calculations for clinical trials

Müller

Huppertz

Dreyhaupt

et al. 2019

Parkinsonism & Related Disorders

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Introduction: A volumetric MRI analysis of longitudinal regional cerebral atrophy in Huntington's disease (HD) was performed as a read-out of disease progression to calculate sample sizes for future clinical trials. Methods: This study was based on MRI data of 59 patients with HD and 40 controls recruited within the framework of the PADDINGTON study and investigated at baseline and follow-up after 6 and 15 months. Automatic atlas-based volumetry (ABV) of structural T1-weighted scans was used to calculate longitudinal volume changes of brain structures relevant in HD and to assess standardized effect sizes and sample sizes required for potential future studies. Results: Atrophy rates were largest in the caudate (-3.4%), putamen (-2.8%), nucleus accumbens (-1.6%), and the parietal lobes (-1.7%); the lateral ventricles showed an expansion by 6.0%. Corresponding effect sizes were-1.35 (caudate),-0.84 (putamen),-0.91 (nucleus accumbens),-1.05 (parietal lobe), and 0.92 (lateral ventricles) leading to N=36 subjects per study group for detecting a 50% attenuation of atrophy for the best performing structure (caudate). A combined score of volume changes in non-overlapping compartments (striatum, parietal lobes, lateral ventricles) increased the effect size to-1.60 and substantially reduced the required sample sizes for detecting a 50% attenuation of atrophy by 10 to N=26 subjects per study group. This combined imaging score correlated significantly both with the CAP score and with the progression of the clinical phenotype. Conclusion: We propose ABV of the striatum together with parietal lobe and lateral ventricle volumes as a combined imaging read-out for progression studies including clinical trials in HD.

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“…In particular, the cost function here depends on the sequence ordering, which to our knowledge standard algorithms do not handle. We therefore derive our own algorithm to fit SuStaIn, based on the well-established methods developed for the event-based model ( 7 , 8 , 42 , 43 ), for which we demonstrate convergence and optimality in simulation (see Supplementary Results: Convergence) and in the data sets used here (see Convergence). As shown in the black box in Supplementary Figure 15 , the SuStaIn model is fitted hierarchically, with the number of clusters being estimated via model selection criteria obtained from cross-validation.…”

Section: Methodsmentioning

confidence: 99%

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

et al. 2018

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The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10−4) or temporal stage (p = 3.96 × 10−5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

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

Cited by 50 publications

References 47 publications

Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt–Jakob disease

Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt–Jakob disease

Combined cerebral atrophy score in Huntington's disease based on atlas-based MRI volumetry: Sample size calculations for clinical trials

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

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