Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Abstract

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer’s disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

“…Sub1 (278 MCI & 85 AD) and Sub4 (164 MCI & 34 AD) have the highest proportion of MCI. The normal anatomy subtype has been confirmed in previous works (Dong et al, 2016b; Ezzati et al, 2020; Jung et al, 2016; Nettiksimmons et al, 2014; Ota et al, 2016; Poulakis et al, 2020, 2018; Ten Kate et al, 2018; Yang et al, 2020). In (Dong et al, 2016b), the authors examined the external validation of the subtypes.…”

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

“…Ferreira et al and follow-up studies from our lab used visual rating scales of brain atrophy in medial temporal, frontal and posterior cortices ( Ferreira et al , 2017 , 2018 , 2019 ; Persson et al , 2017 ; Ekman et al , 2018 ; Oppedal et al , 2019 ; Machado et al , 2020 ) and determined clinical cut points for abnormality ( Ferreira et al , 2015 ). We also used unsupervised clustering in another cross-sectional study by Poulakis et al (2018) , which was recently extended for subtyping on longitudinal data ( Poulakis et al , 2020 ). Other groups used different unsupervised clustering methods ( Noh et al , 2014 ; Dong et al 2015, 2017 ; Hwang et al 2016 ; Na et al , 2016 ; Zhang et al 2016 ; Park et al 2017 ; Varol et al 2017 ), highlighting the methodological variability across studies.…”

“…Surprisingly, no head-to-head comparison of subtyping methods has been published so far. Such a comparison arises as an urgent and important step towards facilitating consistent progress in this field, especially with the current surge in subtyping studies using sMRI investigating subtype or disease progression ( Young et al , 2018 ; Marinescu et al , 2019 ; Poulakis et al , 2020 ) and tau PET ( Whitwell et al , 2018 ; Charil et al , 2019 ; Jeon et al , 2019 ). To illustrate this problem, in the present study, we applied different subtyping methods reported in five previous studies ( Murray et al , 2011 ; Byun et al , 2015 ; Ferreira et al , 2017 ; Risacher et al , 2017 ; Poulakis et al , 2018 ; Charil et al , 2019 ) on sMRI and tau PET data from the same cohort.…”

Towards harmonizing subtyping methods for neuroimaging studies in Alzheimer’s disease

“…Studies from our lab used visual rating scales of brain atrophy in medial temporal, frontal, and posterior cortices (7–13), and determined abnormality based on clinical cut points (14). We also used an unsupervised clustering method in another cross-sectional study (15), which has recently been extended for subtyping on longitudinal data (16). Other groups used different unsupervised clustering methods (17–24), highlighting the methodological variability across studies.…”

“…Surprisingly, no head-to-head comparison of subtyping methods has been published so far. Such a comparison arises as an urgent and important step towards facilitating consistent progress in this field, especially with the current surge in subtyping studies using longitudinal sMRI (16,26,27) and tau PET (3,25,28). To illustrate this problem and substantiate our claim for harmonizing subtyping methods, we applied subtyping methods based on five previous studies (1,5–7,15,25), on sMRI and tau PET data, in the same cohort.…”