More consistently altered connectivity patterns for cerebellum and medial temporal lobes than for amygdala and striatum in schizophrenia

Henning Peters, Junming Shao, Martin Scherr, Dirk Schwerthöffer, Claus Zimmer, Hans Förstl, Josef Bäuml, Afra Wohlschläger, Valentin Riedl, Kathrin Koch, Christian Sorg

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19 Scopus citations

Abstract

Background: Brain architecture can be divided into a cortico-thalamic system and modulatory “subcortical-cerebellar” systems containing key structures such as striatum, medial temporal lobes (MTLs), amygdala, and cerebellum. Subcortical-cerebellar systems are known to be altered in schizophrenia. In particular, intrinsic functional brain connectivity (iFC) between these systems has been consistently demonstrated in patients. While altered connectivity is known for each subcortical-cerebellar system separately, it is unknown whether subcortical-cerebellar systems' connectivity patterns with the cortico-thalamic system are comparably altered across systems, i.e. if separate subcortical-cerebellar systems' connectivity patterns are consistent across patients. Methods: To investigate this question, 18 patients with schizophrenia (3 unmedicated, 15 medicated with atypical antipsychotics) and 18 healthy controls were assessed by resting-state functional magnetic resonance imaging (fMRI). Independent component analysis of fMRI data revealed cortical intrinsic brain networks (NWs) with time courses representing proxies for cortico-thalamic system activity. Subcortical-cerebellar systems' activity was represented by fMRI-based time courses of selected regions-of-interest (ROIs; i.e. striatum, MTL, amygdala, cerebellum). Correlation analysis among ROI-and NWs-time courses yielded individual connectivity matrices [i.e. connectivity between NW and ROIs (allROIs-NW, separateROI-NW), only NWs (NWs-NWs), and only ROIs (allROIs-allROIs)] as main outcome measures, which were classified by support-vector-machine-based (SVM) leave-one-out cross-validation. Differences in classification accuracy were statistically evaluated for consistency across subjects and systems. Results: Correlation matrices based on allROIs-NWs yielded 91% classification accuracy, which was significantly superior to allROIs-allROIs and NWs-NWs (56 and 74%, respectively). Considering separate subcortical-cerebellar systems, cerebellum-NWs and MTL-NWs reached highest accuracy values with 91 and 85%, respectively, while those of striatum-NW and amygdala-NW were significantly lower with about 65% classification accuracy. Conclusion: Results provide initial evidence for differential consistency of altered intrinsic connectivity patterns between subcortical-cerebellar systems and the corticothalamic system. Data suggest that differential dysconnectivity patterns between subcortical-cerebellar and cortical systems might reflect different disease states or patient subgroups.

Original languageEnglish
Article number55
JournalFrontiers in Human Neuroscience
Volume10
Issue numberFEB2016
DOIs
StatePublished - 17 Feb 2016
Externally publishedYes

Keywords

  • FMRI
  • Functional connectivity (FC)
  • Multivariate pattern analysis
  • Schizophrenia
  • Subcortical
  • Support vector machine

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