Auditory oddball deficits in schizophrenia: an independent component analysis of the fMRI multisite function BIRN study

Abstract

Deficits in the connectivity between brain regions have been suggested to play a major role in the pathophysiology of schizophrenia. A functional magnetic resonance imaging (fMRI) analysis of schizophrenia was implemented using independent component analysis (ICA) to identify multiple temporally cohesive, spatially distributed regions of brain activity that represent functionally connected networks. We hypothesized that functional connectivity differences would be seen in auditory networks comprised of regions such as superior temporal gyrus as well as executive networks that consisted of frontal-parietal areas. Eight networks were found to be implicated in schizophrenia during the auditory oddball paradigm. These included a bilateral temporal network containing the superior and middle temporal gyrus; a default-mode network comprised of the posterior cingulate, precuneus, and middle frontal gyrus; and multiple dorsal lateral prefrontal cortex networks that constituted various levels of between-group differences. Highly task-related sensory networks were also found. These results indicate that patients with schizophrenia show functional connectivity differences in networks related to auditory processing, executive control, and baseline functional activity. Overall, these findings support the idea that the cognitive deficits associated with schizophrenia are widespread and that a functional connectivity approach can help elucidate the neural correlates of this disorder.

Fig. 1.

A) An Overview of the Entire Independent Component Analysis (ICA) Starting From the Raw Functional Magnetic Resonance Imaging datasets. The datasets must first be preprocessed before entering an ICA, and the steps used for our specific analysis are listed here. B) Once the datasets are preprocessed, they are reduced using PCA. Each subject's dataset is reduced in the time dimension to a parameter specified during the analysis. These datasets are then concatenated together across time and reduced further to a final number of dimensions, which is equivalent to the final number of components. C) ICA splits this reduced dataset (X) into a mixing matrix (W⁻¹) that represents the ICA timecourses and a set of associated spatial components (C). D) The ICA timecourses undergo a regression with the SPM design matrix to create a set of betas that will be used to determine task relevance and group differences. E) The ICA design matrix used here performs a 2-sample t test with covariates for site and scanner strength.

Fig. 2.

Site Differences by Averaged Beta Weights for 5 Components. Directionality of the beta weights suggests a similar contribution for each site across components.

Fig. 3.

An Overlay of 5 components (C1, C6, C17, C19, and C25) Thresholded at False Discovery Rate P < 1.0 × 10⁻¹⁰. Component maps were generated using a 1-sample random-effects analysis via the SPM5 toolbox. Event-related averages for each component are plotted on the right with separate timecoures for patients (red) and controls (blue dots). Other significant components (C3, C8, and C22) are shown in more detail in later figures.

Fig. 4.

An Overlay of 3 Components That Represent Possibly Auditory Networks (C3, C8, and C13) Thresholded at False Discovery Rate P < 1.0 × 10⁻¹⁰. Areas in red represent an overlap between 2 components where each row represents a distinctive pair of networks. Event-related averages for each component are also shown below with separate timecourses for patients and controls.

Fig. 5.

A Detailed Overlay of Default-Mode Networks (C22 and C23) Thresholded at False Discovery Rate P < 1.0 × 10⁻¹⁰. Red areas represent overlapping regions of activation for both components. Event-related averages below for each component show a negatively modulated ICA timecourse.

Appendix Fig. A1.