Comparison of subtyping methods for neuroimaging studies in Alzheimer’s disease: a call for harmonization

Mohanty, Rosaleena; Mårtensson, Gustav; Poulakis, Konstantinos; Rodriguez‐Vieitez, Elena; Chiotis, Konstantinos; Grothe, Michel J.; Nordberg, Agneta; Ferreira, Daniel; Westman, Eric

doi:10.1093/braincomms/fcaa192

Cited by 31 publications

(67 citation statements)

References 57 publications

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“…Neuroimaging studies have shown topographical conformity and association between tau pathology from tau positron emission tomography (tau-PET) and longitudinal brain atrophy-based neurodegeneration from magnetic resonance imaging (MRI), in cognitively unimpaired individuals [6], prodromal AD and/or AD dementia [4,7,8] and clinical subtypes of AD [9,10]. A critical caveat, however, is the failure to account for heterogeneity in tau-PET topography at a given disease stage (i.e., tau patterns or subtypes) [11][12][13][14][15]. The relationship between tau-PET patterns and atrophy remains unexplored and is important for precision medicine.…”

Section: Introductionmentioning

confidence: 99%

“…We primarily investigated the association between different tau-PET patterns and longitudinal atrophy in the AD continuum (cognitively normal, prodromal AD, AD dementia with Aβ pathology). We secondarily characterized tau-PET patterns on a continuous-scale inspired by the recent conceptual framework [16], compared to and extending beyond the conventional characterization of discrete categorization [13][14][15]17]. We hypothesized that (a) tau-PET patterns would modulate the association between baseline tau-PET and longitudinal atrophy differentially; (b) treating heterogeneity (the different tau-PET patterns) on a continuous-scale over a discretescale could be more appropriate.…”

Section: Introductionmentioning

confidence: 99%

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Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum

Mohanty¹,

Ferreira²,

Nordberg

et al. 2021

Preprint

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INTRODUCTION: Different subtypes/patterns have been defined using tau-PET and structural-MRI in Alzheimer's disease (AD), but the relationship between tau pathology and atrophy remains unclear. Our goals were twofold: (a) investigate the association between baseline tau-PET patterns and longitudinal atrophy in the AD continuum; (b) characterize heterogeneity as a continuous phenomenon over the conventional notion using discrete subgroups. METHODS: In 366 individuals (amyloid-beta-positive: cognitively normal, prodromal AD, AD dementia; amyloid-beta-negative healthy), we examined the association between tau-PET patterns (operationalized as a continuous phenomenon and a discrete phenomenon) and longitudinal sMRI. RESULTS: We observed a differential association between tau-PET patterns and longitudinal atrophy. Heterogeneity, measured continuously, may offer an alternative characterization, sharing correspondence with the conventional subgrouping. DISCUSSION: Site and the rate of atrophy are modulated differentially by tau-PET patterns in the AD continuum. We postulate that heterogeneity be treated as a continuous phenomenon for greater sensitivity over the current/conventional discrete subgrouping.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum

Mohanty¹,

Ferreira²,

Nordberg

et al. 2021

Preprint

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“…We have produced the first comparison of data-driven subtyping results using a disease progression model (SuStaIn) with existing progression-ignorant methods of visual ratings and AVRA. Partial agreement was observed between SuStain and AVRA subtypes on an individual level, and differences may be imputed to the selection of brain regions used to train SuStaIn, that do not cover entirely the same brain region used to assess visual ratings and to a general lack of harmonization of subtyping methods ( Mohanty et al, 2020 ). SuStaIn proved to offer a finer-grained representation of different atrophy patterns as relevant differences in hippocampal volume were observed between subjects from subtypes 1 and 2 that were labeled with minimal atrophy according to the AVRA scores.…”

Section: Discussionmentioning

confidence: 99%

Inter-Cohort Validation of SuStaIn Model for Alzheimer’s Disease

Archetti

Young

Oxtoby³

et al. 2021

Front. Big Data

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Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

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“…In subtype assignments, however, disagreements were considerable. The subtypes therefore need to be further investigated, with an establishment of their harmonization by appropriate methods [ 55 ].…”

Section: Imaging Biomarkers From Ad and Pdmentioning

confidence: 99%

News about the Role of Fluid and Imaging Biomarkers in Neurodegenerative Diseases

Meldolesi

2021

Biomedicines

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Biomarkers are molecules that are variable in their origin, nature, and mechanism of action; they are of great relevance in biology and also in medicine because of their specific connection with a single or several diseases. Biomarkers are of two types, which in some cases are operative with each other. Fluid biomarkers, started around 2000, are generated in fluid from specific proteins/peptides and miRNAs accumulated within two extracellular fluids, either the central spinal fluid or blood plasma. The switch of these proteins/peptides and miRNAs, from free to segregated within extracellular vesicles, has induced certain advantages including higher levels within fluids and lower operative expenses. Imaging biomarkers, started around 2004, are identified in vivo upon their binding by radiolabeled molecules subsequently revealed in the brain by positron emission tomography and/or other imaging techniques. A positive point for the latter approach is the quantitation of results, but expenses are much higher. At present, both types of biomarker are being extensively employed to study Alzheimer’s and other neurodegenerative diseases, investigated from the presymptomatic to mature stages. In conclusion, biomarkers have revolutionized scientific and medical research and practice. Diagnosis, which is often inadequate when based on medical criteria only, has been recently improved by the multiplicity and specificity of biomarkers. Analogous results have been obtained for prognosis. In contrast, improvement of therapy has been limited or fully absent, especially for Alzheimer’s in which progress has been inadequate. An urgent need at hand is therefore the progress of a new drug trial design together with patient management in clinical practice.

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Comparison of subtyping methods for neuroimaging studies in Alzheimer’s disease: a call for harmonization

Cited by 31 publications

References 57 publications

Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum

Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum

Inter-Cohort Validation of SuStaIn Model for Alzheimer’s Disease

News about the Role of Fluid and Imaging Biomarkers in Neurodegenerative Diseases

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