Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach

Lavinia Carmen Uscătescu*, Lisa Kronbichler, Renate Stelzig-Schöler, Brandy-Gale Pearce, Sarah Said-Yürekli, Luise Antonia Reich, Stefanie Weber, Wolfgang Aichhorn, Martin Kronbichler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. https://doi.org/10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.
Original languageEnglish
Pages (from-to)762–778
Number of pages17
JournalBrain Topography
Volume34
Issue number6
Early online date4 Sept 2021
DOIs
Publication statusPublished - Nov 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Schizophrenia
  • Hippocampus
  • Effective connectivity
  • Spectral dynamic causal modelling

Fields of Science and Technology Classification 2012

  • 501 Psychology
  • 305 Other Human Medicine, Health Sciences

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