Table 1.
Studies using data-driven clustering techniques in neuroimaging for characterizing heterogeneity in brain aging, AD and related dementias
| Studies | Modality and sample | Method | Main findings |
|---|---|---|---|
| AD Dementia | |||
| Noh et al., 2014 (33), Na et al., 2016 (117) |
Structural MRI N = 152 AD dementia patients from the Samsung Medical Center (SMC) cohort. |
Ward’s clustering applied on vertex-wise cortical thickness features. | Three distinct cortical thinning subtypes, including medial temporal-dominant (34%), parietal-dominant (18%), and diffuse atrophy (48%). The parietal-dominant subtype was younger and exhibited faster cognitive decline compared to the other subtypes. |
| Hwang et al. 2016 (34) | Structural MRI N = 77 AD dementia patients from ADNI cohort. |
Ward’s clustering applied on vertex-wise cortical thickness features. | Three cortical thinning subtypes: medial temporal (19%), diffuse (56%), and parietal dominant (25%). |
| Dong et al., 2016 (37) | Structural MRI Pooled sample of amnestic MCI (N = 530) and AD dementia (N = 314) patients from ADNI. |
Semi-supervised clustering technique applied on 153 brain ROIs. | Four subtypes: one with minimal atrophy and elevated tau (13%), one with cortical atrophy and proportionately greater executive dysfunction (28%), and two with typical AD atrophy and contribution of vascular disease (59%). |
| Zhang et al., 2016 (39) | Structural MRI N = 188 AD dementia patients from ADNI for model derivation; Factor compositions were inferred in separate amyloid-positive MCI (N = 147) and CN (N = 43) samples. | Bayesian model derived latent atrophy factors from voxel-wise gray matter volume. | Three main factors: temporal atrophy associated with memory decline, neocortical atrophy associated with executive function decline, and subcortical atrophy including striatum, thalamus, and cerebellum, that was associated with the slowest cognitive decline across domains. Subjects were assigned to the different factors in a probabilistic manner |
| Park et al., 2017 (35) | Structural MRI N = 225 AD dementia patients from the SMC cohort; Validation dataset of N = 131 AD dementia patients from ADNI. |
Graph-theory-based clustering approach (Louvain method) applied on vertex-wise cortical thickness features. | Three subtypes with high reproducibility (>90%): parietal-predominant (P; 35%), medial temporal-predominant (MT, 36%), and diffuse (D, 29%) atrophy. The P subtype showed the worst clinical presentation across cognitive domains. |
| Poulakis et al., 2018 (38) | Structural MRI N = 299 AD dementia patients, pooled from the multicentric AddNeuroMed and ADNI cohorts. |
Unsupervised random forest-based clustering of volume measures from 162 atlas-defined cortical and subcortical regions. | Two distinct ‘typical’ subtypes (72%), as well as three different atypical subtypes (29%), including limbic predominant, minimal atrophy, and hippocampal sparing. |
| Ten Kate et al., 2018 (36) | Structural MRI N = 299 amyloid-positive AD dementia patients in discovery cohort, and two independent validation datasets (N = 181; N = 227). Subtype classification in N = 603 amyloid-positive MCI patients. |
Nonnegative matrix factorization of gray matter volumes from 1024 equally sized cortical and subcortical area parcellations. | Four atrophy subtypes: (i) medial-temporal predominant with worst memory and highest vascular lesion burden (19%); (ii) parieto-occipital atrophy with poor executive and visuospatial functioning (28%); (iii) mild atrophy with best cognitive performance, but highest CSF tau levels (35%); (iv) diffuse cortical atrophy (18%). Subtype classifications in amyloid-positive MCI patients (‘prodromal AD’) showed similar biomarker characteristics as the AD dementia subtypes. |
| Young et al., 2018 (41) | Structural MRI Two separate samples of pooled AD dementia, MCI, and CN subjects from ADNI (N = 793 and N = 576, respectively)1. |
Combined clustering and event based modeling of 13 atlas-defined subcortical and cortical volumes. Algorithm derives subtypes together with their estimated stage. | Three distinct spatiotemporal atrophy patterns defined by differential atrophy start: (i) typical, starting in the medial temporal lobe; (ii) cortical, starting in temporo-frontal areas; (iii) subcortical, starting in the basal ganglia. |
| Sui et al., 2018 (46) | Diffusion Tensor MRI N = 48 AD dementia patients from ADNI for model derivation; Factor compositions were inferred in separate samples of MCI (N = 134) and CN subjects (N = 50). |
Latent Dirichlet Allocation of voxel-wise fractional anisotropy values within the brain’s white matter. | Three latent factors of microstructural white matter impairment categorized subjects in a probabilistic fashion: (i) temporo-frontal; (ii) parietal; and (iii) long fiber bundle factor (corpus callosum, superior longitudinal fasciculus). Latent factors had differential associations with memory and executive function decline. |
| Lowe et al., 2018 (45) | Tau-PET Pooled sample of N=86 amyloid-positive amnestic MCI (N = 35) and AD dementia (N = 51) patients from the Mayo Clinic cohorts. |
Ward’s hierarchical clustering applied to 47 regional Tau-PET (flortaucipir) values. | Three clusters mainly reflecting incremental involvement of medial temporal (57%) to temporo-parietal (36%) to frontal lobe regions (7%). Cluster with highest and most widespread Tau-PET signal had younger age at onset. |
| Whitwell et al., 2018 (47) | Tau-PET N = 62 amyloid-positive AD dementia patients. |
K-median clustering of Tau-PET (flortaucipir) values from entorhinal and neocortical composite ROIs. | Three clusters: (i) low entorhinal and cortical (34%), (ii) low entorhinal but high cortical uptake (34%), and (iii) high cortical and entorhinal uptake (32%). Cluster (ii) had lowest prevalence of APOE4 carriers and highest percentage of atypical AD presentations. |
| Varol et al 2017 (118) | Structural MRI N = 123 AD dementia patients from ADNI. |
Non-linear learning algorithm for classification and subtyping applied on 153 brain ROIs. | Three subtypes: (i) diffuse (24%), (ii) precuneus and temporal lobe atrophy with some prefrontal involvement (51%), and (iii) predominant atrophy in the hippocampus and medial temporal lobe (25%). |
| Sun et al. 2019 (116) | Combined structural MRI and neuropsychologic testing. Two separate samples of AD dementia patients from ADNI used as discovery (N = 149) and replication (N = 170) cohorts. |
Hierarchical Bayesian clustering applied simultaneously on voxel-wise gray matter volumes and neuropsychological test scores. | Three atrophy-cognitive factors categorized subjects in a probabilistic fashion: (i) medial temporal atrophy with episodic memory deficits; (ii) lateral temporal atrophy with language deficits; (iii) posterior cortical atrophy with visuospatial and executive function deficits |
| Jeon et al. 2019 (48) | Combined structural MRI, Tau-PET, and Amyloid-PET. N = 83 AD dementia patients. | Hierarchical clustering applied on fused features from vertex-wise cortical thickness, Tau-PET (THK5351), and Amyloid-PET (flutemetamol). | Three distinct subtypes of overlapping Tau-PET signal and structural atrophy: (i) medial temporal-dominant (53%), (ii) parietal-dominant (23%), and (iii) diffuse subtype (24%). Regional amyloid pattern did not differ across subtypes. |
| MCI and other at risk populations | |||
| Jung et al. 2016 (80) | Structural MRI N = 613 older individuals with subjective memory impairment from SMC cohort. |
Ward’s hierarchical clustering of vertex-wise cortical thickness values. | Three cortical thickness subtypes: (i) no/minimal atrophy (52%), (ii) diffuse atrophy (35%), and (iii) AD-like temporal atrophy (13%). No/minimal atrophy subtype had higher prevalence of depression; AD-like atrophy subtype had highest age, more vascular risk factors, and lowest cognitive performance. |
| Eavani et al.,2018 (12) | Combined structural MRI and resting state fMRI. N = 400 cognitively normal elderly subjects from the Baltimore Longitudinal Study of Aging (BLSA) cohort. |
“Mixture of Experts” clustering applied on combined voxel-wise gray matter volume and functional connectivity features for subjects who deviated from normative brain aging trajectories. | Five subtypes of accelerated brain aging: (i) AD-like changes with contribution of vascular brain injury (27%); (ii) fronto-orbital atrophy but increased functional measures (15%); (iii) high brain tissue reserve counterbalancing brain loss in early AD (24%); (iv) pattern similar to those found in patients with Lewy body pathology (20%); (v) reduced function in motor network (14%). |
| Habes et al., 2018 (44) | Combined FLAIR and T1-MRI. N = 1836 participants across adult age range from population-based cohort study (SHIP); Replication sample of N = 307 CN and MCI participants from BLSA cohort. |
Nonnegative matrix factorization applied on white matter hyperintensity maps. | Four distinct regional distribution patterns of vascular lesions: (i) Frontal periventricular; (ii) posterior periventricular; (iii) dorsal periventricular; (iv) deep lesions. Frontal periventricular lesions showed strongest association with distributed gray matter atrophy, dorsal periventricular lesions with AD polygenic risk score. |
| Kim et al. 2019 (82) | Structural MRI N = 662 amnestic MCI patients from SMC cohort. |
Graph-theoretical clustering method (Louvain method) applied on vertex-wise cortical thickness features. | Three subtypes: (i) no/minimal (39%), (ii) medial-temporal (31%), and (iii) parieto-temporal (30%). Parieto-temporal subtype had highest frequency of APOE4 carriers and amyloid PET positivity, and elevated risk of dementia conversion. |
| Ezzati et al. 2019 (81) | Structural MRI N = 696 amnestic MCI patients from ADNI. |
Latent class analysis (LCA) applied on atlas-based regional volumes, decomposed into the 10 most informative ROIs using principal component analysis. |
Four aMCI subgroups were found: (i) most similar to normal controls in brain structure and function (58%), (ii) with characteristics similar to early AD (33%), (iii) with highest global and medial temporal atrophy and worst overall cognitive performance (5%), and (iv) minimal atrophy but poor executive function performance (4%). |
| Frontotemporal Dementia | |||
| Whitwell et al., 2009 (86) Whitwell et al., 2013 (87) |
Structural MRI N = 66 bvFTD patients. |
Ward’s clustering applied on gray matter volumes from 26 cortical and subcortical ROIs. | Four distinct atrophy subtypes: two were characterized by predominant temporal lobe atrophy (‘temporal-dominant’ (9%) and ‘temporofrontoparietal’ (41%)), the other two by predominant frontal lobe atrophy (‘frontal-dominant’ (32%) and ‘frontotemporal’ (18%)). |
| Cerami et al. 2016 (88) | FDG-PET N = 52 bvFTD patients. |
Ward’s clustering applied on FDG-PET measurements from 16 cortical and subcortical ROIs. | Two major hypometabolism variants: a “frontal” variant with predominant executive and language deficits (48%), and a “temporo-limbic” variant with poor performance on long-term memory tasks (52%). |
| Ranasinghe et al., 2016 (89) | Structural MRI N = 104 bvFTD patients. |
Euclidian distance-based clustering applied on gray matter volumes from 18 preselected temporo-frontal network ROIs. | Four clusters of network-specific atrophy: two involved the fronto-insular salience network (‘frontal’ (31%) and ‘frontotemporal’ (25%)), and another two involved either subcortical-predominant (35%) or anterior temporal semantic-appraisal network features (9%). |
| Matias-Guiu et al., 2018(90); Matias-Guiu et al., 2019 (91) |
FDG-PET N = 91 PPA patients, including non-fluent, semantic, and logopenic variants. |
Ward’s clustering applied on FDG-PET measurements from 116 atlas-defined brain regions. | Differential hypometabolic patterns separated the three clinical PPA variants, but suggested a further splitting of non-fluent and logopenic variants into two subtypes each, which also differed in their specific language deficits. |
| Young et al., 2018 (41) | Structural MRI N=1722 presymptomatic and symptomatic carriers of autosomal-dominant FTD mutations from the multicentric GENFI study. |
Combined clustering and event based modeling of 13 atlas-defined subcortical and cortical volumes. Algorithm derives subtypes together with their estimated stage. | Four distinct spatiotemporal patterns of atrophy progression in familial FTD, which had high correspondence with genetic subtypes. |
| Caminiti et al. 2019 (119) | FDG-PET N=72 DLB patients |
Ward’s clustering applied on FDG-PET measurements from 14 occipital, parietal, and temporal ROIs known to be involved in DLB. | Two clusters characterized by slightly different occipital hypometabolism. Cluster with more severe occipital involvement (43%) had worse global cognition and higher risk for developing visual hallucinations. |
| Lewy body dementia | |||
| Uribe et al. 2016 (106); Uribe et al. 2019 (108) |
Structural MRI N=88 nondemented PD patients |
Ward’s clustering applied on vertex-wise cortical thickness features. | Three distinct clusters: (i) non-atrophic (33%), (ii) parieto-temporal (34%), and (iii) occipital-frontal (33%) cortical atrophy. The parieto-temporal atrophy subtype resembled an AD-typical pattern and showed lowest cognitive performance. |
| Uribe et al. 2018 (107) | Structural MRI N=77 de novo PD patients from PPMI |
Ward’s clustering applied on 360 atlas-based cortical thickness features. | Two distinct patient subgroups in this early disease stage: one with mainly anterior temporal-frontal atrophy (43%), and the other with predominant posterior parieto-occipital atrophy (57%) that also showed lower memory scores. |
1
Number refers to initial sample size before additional quality control procedures
2
Number refers to sample size before additional quality control procedures