. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun. 2018 Oct 15;9(1):4273. PubMed.


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  1. This study describes SuStaIn, a very interesting new machine-learning approach that incorporates both temporal (i.e., disease stage) and phenotypic (i.e., disease topography) dimensions toward modeling MRI data in genetic FTD and sporadic AD. The main advance is the inclusion of both temporal and phenotypic dimensions in a single model, overcoming a limitation of previous approaches which considered only one of these dimensions at a time.

    The study convincingly shows that SuStaIn performs better than traditional machine learning in classifying genetic subtypes of FTD in the GENFI study, and in predicting conversion from MCI to AD in ADNI. I find the data a bit less convincing in terms of the utility of SuStaIn for individual classification at the earliest disease stages—it seems like there the classification probabilities are centered in an “uncertain zone” in unaffected FTD mutation carriers and patients with MCI (Figure 5). This may represent a limitation of MRI, which is inherently a later-stage disease biomarker, rather than a limitation of the model itself. It will be interesting to see how the model performs compared with true longitudinal data in inferring stage-related progression of atrophy—these data should be available in ADNI and GENFI—as well as with other neuroimaging modalities.

    Finally, as acknowledged by the authors, disease heterogeneity is more complex than just topography and stage. It will be exciting to see how the boundaries of machine learning can be pushed even further to capture the complexity of these biological phenomena.

    View all comments by Gil Rabinovici
  2. This is a very good paper. I completely agree with the main finding that segmentation of the AD population is a relevant first step for better analysis of this disease. All our previous experiments on the ADNI dataset demonstrated that there exist strong differences among MCI and AD subpopulations (see references below). From the paper I cannot conclude if subpopulations we have managed to detect are well in agreement with the subpopulations that have been detected in here by Young et al.

    The methodology they use is novel and potentially very relevant. It is a statistical approach to clustering (segmentation) of data sequences in a multiview setting. A known fact about all clustering approaches is that there is no objective measure to validate them and there is no exact way to determine if a result of one methodology is better than a result produced by another methodology. The ultimate measure of the quality is only usefulness of the results for the medical practice. We have previously shown how computational AD subtyping can be validated using longitudinal clinical outcomes. Our unbiased multilayer clustering algorithm identified, in the same ADNI data set, a cluster of people with markedly greater brain atrophy and clinical decline rates. Such a cluster is highly suitable for clinical trials (Gamberger et al., 2017). 


    . Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease. Sci Rep. 2017 Jul 28;7(1):6763. PubMed.

    . Homogeneous clusters of Alzheimer's disease patient population. Biomed Eng Online. 2016 Jul 15;15 Suppl 1:78. PubMed.

    . Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Brain Inform. 2016 Sep;3(3):169-179. Epub 2016 Mar 30 PubMed.

    View all comments by Dragan Gamberger
  3. It is a curious fact that most radiologists, and even most neurologists, when presented with an MRI scan of a patient suspected to have a neurodegenerative disease, rarely pay much attention to the morphology of the brain, other than to comment on hippocampal and/or gross lobar atrophy. The study by Alexandra Young and colleagues informs interested neuroscientists everything they would want to know about patterns of regional brain atrophy, but were afraid to ask. The study is an exhaustingly detailed report describing the heterogeneity of patterns of regional brain atrophy in two major neurodegenerative diseases and their subtypes, namely frontotemporal dementia and Alzheimer’s disease.

    It is not, however, enough to merely recognize patterns of regional atrophy, which are characteristic of the subtypes of these two groups of diseases. The patterns of atrophy change as the disease progresses, thus providing diagnostic information not only about the disease subtype, but also about the stage of the disease. The authors have used a machine-learning technique on MRI volumetric data from a large number of cases derived from the Genetic FTD Initiative (GENFI) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to disentangle patterns of atrophy that are identifying characteristics of each subtype in these two disease groups and of the disease stage, hence “Subtype and Stage Inference” or “SuStaIn.”

    One might ask how this interesting technique has utility beyond providing information about subtype and stage.  It turns out that the some of the genetic heterogeneity of the frontotemporal dementias can be identified by recognition of the phenotypic heterogeneity. Within each genetic subtype, an even more fine-grained analysis using this method allows discovery of further phenotypic heterogeneity.

    In Alzheimer’s disease, by recognizing patterns of atrophy for each of three major subtypes of disease (limbic predominant, hippocampal sparing, and typical, which is a combination of the first two subtypes), it is possible to identify the stage of the disease, and most likely to predict expected rate and pattern of progression of the disease. This information could be useful to the clinician who is often asked by the patient to predict the rate of progression of the disease. Further, accounting for this heterogeneity in the expected individual rates of cognitive and functional progression could be very useful in analyzing effectiveness of an intervention in clinical research trials, much as knowledge of the APOE genotype has made a large impact in analysis of Alzheimer clinical trials.

    View all comments by Ranjan Duara
  4. The study is well-done and mathematically rigorous. Some of the assumptions seem a bit generous—it likely isn’t true that biomarkers have a discrete and stepwise progression, uniform priors isn’t really accurate, etc., and the feature set seems a little sparse—they only consider volumes from a few major areas and we already know that those volumes do correspond to the disease. That said, it is an ambitious attempt to integrate our knowledge of one of the few well-recognized phenotypes (cerebral atrophy) with a comprehensive chronological model of disease progression. It is conceivable that such a model could help us improve our ability to diagnose patients with this disease and, most importantly, lead to better cohort selection for clinical trials. Many clinical trials of drugs for these diseases, particularly Alzheimer’s, suffer from poor phenotyping. SuStaIn is a step toward changing that.

    View all comments by Eric Oermann

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  1. Across Time and Space: Machine Learning Reveals Paths to Dementia