DIFFERENCES IN THE BRAIN GLUCOSE METABOLISM BETWEEN SCHIZOPHRENIA PATIENTS AND HEALTHY CONTROLS: A GRAPH NETWORK ANALYSIS FROM 2-[18F]-FDG-PET DATA
Authors: De Simone *Giuseppe , Ciccarelli Mariateresa , Vellucci Licia , Mazza Benedetta , Iasevoli Felice , Pappatà Sabina , De Bartolomeis Andrea 
Published: February 2025

Background

Schizophrenia is a worldwide severe mental illness in which alterations at the synaptic level play an essential role in the underpinning neuropathology [1]. Impairments in synaptic spines result in ineffective data processing in the whole brain with detrimental effects on network measures [1]. Aberrant processing and integration of neuronal information could directly lead to the onset of psychotic symptoms by affecting the functioning of the Central Executive, Salience, and Default Mode Network [2].

Aims

The present work aims to explore differential patterns of functional brain organization by applying PET-18FDG and, subsequently, for the first time in TRS patients, network analysis and graph theory applications.

Methods

Twenty-eight schizophrenia responders (nTRS), 26 treatment-resistant patients (TRS), and 16 healthy controls (HC) underwent 2-[18F]-FDG-PET. Maps of relative brain glucose metabolism were processed in the AAL-Merged atlas template to obtain data from 65 brain regions. Inter-subject connectivity matrices were obtained by the computation of partial correlations through Gaussian graphical models (GGMs). To get a sparser network and narrow the “shrinkage” effect, an arctangent type penalty (atan), which penalizes smaller partial correlations and saves larger partial correlations from regularization, was applied. The previous analyses, as well as numeric descriptions of group networks, were performed by the “GGMncv” and “igraph” packages in R [3, 4].

Results

A global reduced connectivity was found in the nTRS and TRS groups compared to HC as testified by the decreased number of edges, edge density, global clustering coefficient, and small- worldness index. The main alterations were localized at the frontal lobe, default-mode network, and dorsal dopamine pathway (please, see figure). The most heavily penalized nodes were the right inferior frontal region, insula, and posterior cingulate cortex as well as the left parietal lobule, fusiform gyrus, thalamus, and putamen.

Discussion

The hypo-connectivity pattern detected in schizophrenia patients replicated previous fMRI findings [2]. Otherwise, hyperactivity of DMN was revealed in first-episode, drug-naï ve patients [5]. Patients who discontinued antipsychotics showed higher global functional connectivity compared to HC [6]. We concluded that the decreased connectivity may be associated with chronic therapy while differences between nTRS and TRS need to be better defined by future subanalyses.

References

1.Obi-Nagata, K., Y. Temma, Synaptic functions and their disruption in schizophrenia: From clinical evidence to synaptic optogenetics in an animal model. Proc Jpn Acad Ser B Phys Biol Sci, 2019. 95(5): p. 179-197.

2.Dong, D., Y. Wang, Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity. Schizophrenia Bulletin, 2018. 44(1): p. 168-181.

3.Williams, D.R., GGMncv: Nonconvex Penalized Gaussian Graphical Models in R. 2020.

4.Csá rdi, G., Nepusz, T., Mü ller, K., Horvá t, S., Traag, V., Zanini, F., &Noom, D., igraph for R: R interface of the igraph library for graph theory and network analysis (v1.5.0.1). Zenodo, 2023.

5.Guo, W., F. Liu, Hyperactivity of the default-mode network in first-episode, drug-naive schizophrenia at rest revealed by family-based case-control and traditional case-control designs. Medicine (Baltimore), 2017. 96(13): p. e6223.