Distinctive Whole-brain Cell-Types Strongly Predict Tissue Damage Patterns in Eleven Neurodegenerative Disorders

Reviewer #1 (Public Review):

This study explores the relationship between the most common spatial patterns of neurodegeneration and the density of different cell types in the cerebral cortex. The authors present data showing that atrophy patterns in Alzheimer's disease and Frontotemporal dementia strongly associate with the abundance of astrocytes and microglia. While the results here may be considered preliminary, this work takes a step in the right direction by emphasizing the critical role that cells other than neurons play in the degeneration patterns observable with neuroimaging.

I have two main comments:

1. The authors make an important innovation by applying the cellular deconvolution approach to create brain-wide maps of cellular abundance, and then comparing these maps to atrophy patterns from the most common neurodegenerative diseases and dementia syndromes.

2. I would have preferred to see more figures with brain images showing the cellular abundance maps and the atrophy maps. Without being able to see these figures, it's difficult for the reader to 1) validate the atrophy patterns or 2) gain intuition about how the cellular abundance maps vary across the brain. The images in Figure 1C give a small preview, but I'd like to see these maps in their entirety on the brain surface or axial image slices.

Reviewer #2 (Public Review):

Pak et al. report on a study using a computational method to assess differences in the relative proportion of six canonical brain cell types, across eleven neurodegenerative classes (defined as both clinical syndromes (e.g. FTD, PD), groups of neurogenerative diseases (e.g. 4-repeat tauopathies) or distinct neuropathological entities (e.g. FTLD-TDP type C), as they relate to a standard map of class-dependent volume loss. The study uses innovative methods and is commendable in its goal to highlight the contribution of non-neuronal cell types to the pathobiology of neurodegeneration. The findings of the study are in part contradicting expected results based on extensive literature on the biology of these diseases. The authors based their methodology on the use of a deconvolutional cell classifier; however, do not extensively recognize that their data on gene expression are based on normal brain levels rather than on diseased ones. Also, while predicted levels are uniquely based on patterns of brain atrophy, it is not possible to know whether this strategy is generalizable to all diseases (for instance, it is known that pure DLB, PD and ALS are not associated with extensive brain atrophy), or even adequately comparable between subtypes of diseases within the same class (e.g., different forms of FTLD). The authors do not acknowledge that only data based on true neuropathological assessment may prove whether their findings are true. Subject characteristics, numbers, and diagnostic criteria are hard to assess and only described in the methods section. This format prevents the reader from assessing data robustness while going through the results, especially when fundamental biological bases of nomenclature and differences between clinical syndromes and pathological entities are omitted or uncharacteristically provided.

Reviewer #3 (Public Review):

This study is a fine example of a recent productive trend in the integration of neuroimaging and molecular biology of the brain: in brief, overlaying some neuroimaging data (usually from a large cohort) onto the high spatial resolution gene expression in the Allen Human Brain Atlas data, derived from 6 individuals. By projecting structural MRI images over cell type proportions identified in the Allen data, the authors can represent various diseases in terms of their spatially-associated cell types. The result has implications for prioritizing the contributions of various cell types to each disease and creates an even-handed cell type profile through which the 11 diseases can be compared.