Supplementary analyses to “A network psychometric approach to neurocognition in early Alzheimer's disease”: Differential variability, community structure, and statistical tests

Abstract

In 2021, I published an Exploratory Report entitled “A network psychometric approach to neurocognition in early Alzheimer’s disease” in Cortex. In this paper, I created and analysed network models of neuropsychological task scores in cognitively normal (CN), amnestic mild cognitive impairment (aMCI), and early Alzheimer’s disease (eAD) groups. I also generated four hypotheses:•Original Hypothesis 1: Episodic memory variables will be most central in confirmatory network models of aMCI.•Original Hypothesis 2: Category Fluency becomes increasingly central in network models of groups with more severe Alzheimer’s disease (AD).•Original Hypothesis 3: Memory-semantic-language and attention-speed-working memory clusters will emerge in confirmatory network models for aMCI, eAD, and AD groups. They will be more pronounced for groups with more severe AD.•Original Hypothesis 4: Semantic networks underlying Category Fluency performance support the acquisition of word list memoranda in aMCI and eAD. After publishing my original paper, I learned of differential variability, further statistical tests, and community detection algorithms relevant to these hypotheses. In this commentary, I report the results of supplementary analyses based on the same data and network models in my original paper. Accordingly, these supplementary analyses do not provide confirmatory evidence for my original hypotheses. Instead, they reflect an attempt ratify or refine my original hypotheses based on additional analytical techniques. Indeed, the results of these supplementary analyses prompt some minor but important revisions to my original hypotheses; I present revised hypotheses throughout this commentary. R code and output for all supplementary analyses is accessible online: https://osf.io/2a7uz/.

The influence of working memory and processing speed on other aspects of cognitive functioning in de novo Parkinson's disease: Initial findings from network modelling and graph theory

“…Impressions of cognitive functioning can be drawn from the parameters and characteristics of network models based on observable task scores, although the correspondence between these latent and observed levels is clearly imperfect . Studies have used network models of neuropsychological task data to investigate cognitive functioning in several neurological disorders (for a review see , including Alzheimer's disease (Ferguson, 2023;Ferguson & Alzheimer's Disease Neuroimaging Initiative, 2021;Tosi et al, 2020;Wright et al, 2021), temporal lobe epilepsy (Garcia-Ramos et al, 2021), stroke (Massa et al, 2015) and frontal lesions ( Jonker et al, 2019), but network modelling is yet to be applied in dnPD.…”