Sensory and cross-network contributions to response inhibition in patients with schizophrenia

Abstract

Patients with schizophrenia show response inhibition deficits equal to or greater than those seen in impulse-control disorders, and these deficits contribute to poor outcome. However, little is known about the circuit abnormalities underlying this impairment. To address this, we examined stop signal task performance in 21 patients with schizophrenia and 21 healthy controls using event related potential (ERP) and resting state functional connectivity. Patients showed prolonged stop signal reaction time (SSRT) and reduced N1, N2, and P3 amplitudes compared to controls. Across groups, P3 amplitudes were maximal after SSRT (i.e., after the time associated with the decision to stop occurred), suggesting that this component indexed response monitoring. Multiple regression analyses showed that longer SSRTs were independently related to 1) patient status, 2) reduced N1 amplitude on successful stop trials and 3) reduced anticorrelated resting state functional connectivity between visual and frontoparietal cortical networks. This study used a combined multimodal imaging approach to better understand the network abnormalities that underlie response inhibition in schizophrenia. It is the first of its kind to specifically assess the brain's resting state functional architecture in combination with behavioral and ERP methods to investigate response inhibition in schizophrenia.

Keywords: EEG; Impulsivity; Resting state functional connectivity; Schizophrenia; Stop signal task.

Fig. 1

A) Timeline relative to onset of stop signal. Onset of go and stop stimuli are shown. Left topographic maps shows N1 to successful gos for controls, right topographic maps shows N1 to successful stops for controls. B) ERP topography for controls (CN), patients (PT), and controls–patients (Cn-Pt) for N1 component for successful gos (left; 0.2 μV/step) and successful stops (right; 0.5 μV/step). Critical electrodes are shown in green. C) Grand average for successful go N1 component (left) and successful stop N1 component (middle). Time windows are shown in gray. Right panel shows correlation between N1 amplitude (successful stops) vs. SSRT.

Fig. 2

A) Timeline as in Fig. 1A. Left topographic maps shows N2 to unsuccessful stops for controls, right topographic map shows P3 to successful stops for controls. B) ERP topography for unsuccessful stop N2 component (left) and successful P3 component (right). C) Grand average maps for N2 to unsuccessful stops (left), P3 to successful stops (middle), Right: Correlation between SSRT and P3 to successful stops. We used a late window for the N2 component to avoid overlap with the N1 potential.

Fig. 3

A) Left: Network maps and right: group differences in network homogeneity for networks from Yeo et al. (2011), Vis = visual (purple), Somat = Somatomotor (blue), DA = dorsal attention (green), VA = ventral attention (violet), Limbic (cream), FP = frontoparietal (orange), and DMN = default mode network (salmon), B) Group differences for network homogeneity for each network, C) Correlation between SSRT and Visual/FP networks, Inset: Visual (purple) and frontoparietal (orange) networks, D) Correlation between raw SSRT and Predicted SSRT, accounting for N1, and Visual/FP RSFC.