Association between gene expression and functional‐metabolic architecture in Parkinson's disease

Abstract

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional‐metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age‐and‐sex‐matched healthy controls for simultaneous 18F‐FDG‐PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional‐metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional‐metabolic architecture in PD. Between‐group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR < .048) in PD. Increased FDG‐uptake was found in the somatomotor and ventral attention network while decreased FDG‐uptake was found in the visual network (pFDR < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10−8). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging‐defined brain functional‐metabolic architecture of PD.

Functional gradients reveal cortical hierarchy changes in multiple sclerosis

“…Instead, the secondary gradient showed a gradual axis of connectivity variation, with the visual network on one end and the DMN on the other, with intermediary networks in between, similar to the principal gradient previously identified in (Margulies et al, 2016). This discrepancy has been previously observed and attributed to different factors, including preprocessing strategies (Hong et al, 2020;Knodt et al, 2023), age range of the studied population (Bethlehem et al, 2020;Hu et al, 2022), and sample size (Zang et al, 2023), whereas it should be independent from parcellation resolution (Vos de Wael et al, 2020). In this study, participants were relatively young, thus suggesting that age should not significantly affect FGs; on the other hand, the sample size was not as small as in other works reporting the canonical FGs (Lee et al, 2023;Zhang et al, 2022).…”

“…In the resulting embedding space, cortical nodes that exhibit stronger interconnections, either through numerous connections or a few very strong connections are positioned closely together, whereas nodes with weaker or no connections are situated farther apart. For the sake of interpretability, we focused on the first two FGs, in line with previous works (Petersen et al, 2022;Xiao et al, 2023;Zang et al, 2023), as these will describe more variance of the overall FC data and identify the two main cortical hierarchies previously described in literature (Margulies et al, 2016). A gradient template was constructed using a group-averaged FC matrix from the data sets of all MS and HC individuals.…”