Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia

Abstract

Although a number of recent studies have examined functional connectivity at rest, few have assessed differences between connectivity both during rest and across active task paradigms. Therefore, the question of whether cortical connectivity patterns remain stable or change with task engagement continues to be unaddressed. We collected multi-scan fMRI data on healthy controls (N=53) and schizophrenia patients (N=42) during rest and across paradigms arranged hierarchically by sensory load. We measured functional network connectivity among 45 non-artifactual distinct brain networks. Then, we applied a novel analysis to assess cross paradigm connectivity patterns applied to healthy controls and patients with schizophrenia. To detect these patterns, we fit a group by task full factorial ANOVA model to the group average functional network connectivity values. Our approach identified both stable (static effects) and state-based differences (dynamic effects) in brain connectivity providing a better understanding of how individuals' reactions to simple sensory stimuli are conditioned by the context within which they are presented. Our findings suggest that not all group differences observed during rest are detectable in other cognitive states. In addition, the stable differences of heightened connectivity between multiple brain areas with thalamus across tasks underscore the importance of the thalamus as a gateway to sensory input and provide new insight into schizophrenia.

Keywords: Connectivity; Posterior temporal areas; Schizophrenia; Static & dynamic connectivity; Thalamus; fMRI.

Fig.1

Schematic of the analysis pipeline

Fig-2

Maps of the components identified as non-artifactual in static FNC or dynamic FNC analysis: Of the 75 components returned by the GICA, 45 were identified as non-artifactual components. Only 34 of these non-artifactual components showed static FNC or dynamic FNC effects. 34 non-artifactual components are divided into groups based on their anatomical and functional properties and include visual network, thalamic network, cerebellar network, frontal network, attentional network, default mode network, sensory motor network, and Auditory networks.

Fig-3

A) static FNC matrix(lower part). Pairwise correlations of component pairs showed static FNC effects at the α> 0.001 level. B) dynamic FNC matrix(upper part). Pairwise correlations of component pairs showed dynamic FNC effects at the α≤ 0.001 level. C-D) Samples for static FNC; thalamus (IC12) / auditory networks (IC38), attentional network (IC32) / default mode network (IC68).E-F-G-H) Sample for dynamic FNC; attentional network (IC26) / cerebellar network (IC24), frontal network (IC01) / visual network (IC69), attentional network (IC35) / auditory network (IC71), visual network (IC35) /cerebellar network(IC24)