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  1. Evaluation Summary:

    This work is of interest to neuroscientists and medical professionals involved in the study of Alzheimer's disease and related neurodegenerative conditions. The findings provide important information about how potential network-based structural and metabolic imaging biomarkers are associated with memory performance during distinct disease stages, in line with previous hypothetical biomarker models. The study is conceptually and methodologically sound, although some aspects of the analysis and reporting of the results could be further clarified.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  2. Reviewer #1 (Public Review):

    In this study, the authors use T1-weighted structural MRI and FDG-PET data from an open access cohort to estimate individual-level structural and metabolic covariance networks across the Alzheimer-continuum using well-established sites of Alzheimer's pathology as seeds, detecting clear differences between diagnostic groups. They proceed to show that the evolution of these networks along the disease continuum is associated with memory performance in a nonlinear manner, with different trajectories. The results provide insight into structural and metabolic covariance network contributions to memory performance throughout the disease course, which adds to the current knowledge about potential network-based biomarkers and might have relevance to evaluating these markers in a clinical setting.

    - The analyses were performed on a well-characterised cohort with an adequate sample size that contributes to the robustness of the results.
    - Taking into account the non-linearity of the association between network-based descriptors and memory performance conforms better to current hypothetical models of biomarker dynamics in Alzheimer's disease.

    - It is encouraging that the results were replicated in a validation dataset, however, based on the manuscript it seems it is not independent from the main analysed data. Since the individual network estimation step relies on back-projection from a group-level salience map, the generalisability of the results might be better assessed by keeping the validation dataset independent.
    - Along this line, the employed method estimates individual network scores that quantify the relative contribution of a certain participant to the group-level salience map. This could potentially sensitise the method to the relative imbalance of group sizes across diagnoses and/or A/T categories, or to outliers.

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  3. Reviewer #2 (Public Review):

    The authors present an analysis of multi-modal imaging data from several hundred individuals who range from cognitively normal to mild cognitive impairment to Alzheimer's diagnosis. The authors extract biomarkers of structural (anatomical MRI) and functional (via PET imaging) integrity in memory networks and show these biomarkers have varied trajectories in their relationship to memory metrics. Specifically, individuals with amyloid pathology had an early contribution of structural biomarkers in the hippocampus to memory metrics and a later influence of angular gyrus related metabolic networks while individuals with non-amyloid pathology had no strong relationships with either structure or metabolism.

    Importantly, they replicated their findings in a separate cohort of individuals which strengthens the confidence in the results (although this replication dataset appears to be mostly containing the data from the original analyses). Overall, the paper points to differential disease progression in amyloid versus non-amyloid Alzheimer's progression, which may have implications for targeted treatment of these differing patient subgroups or more accurate tracking of treatment effects. The analyses and the results largely support their conclusions (but do need to be replicated using another non-sparse prior), which could be used to guide targeted therapies and track the efficacy of treatments.

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