Using ICA, we identified multiple functionally connected networks involved in auditory target detection. Furthermore, we showed significant differences in schizophrenia patients in many of the networks that include regions commonly implicated in the illness, such as the bilateral temporal lobes, default-mode regions, and DLPFC. Noticeable decreases in the positive modulation of the ICA timecourses in patients suggest decreased brain activity relative to controls during auditory target detection, which might be further linked to the aberrant activity we found in the DMN. Abnormalities in multiple networks in schizophrenia suggest that the level of cognitive dysfunction is diffuse and widespread. This provides further support of theories suggesting that aberrant brain connectivity is a significant biological marker of the pathophysiology of schizophrenia.11,24,31–33
Consistent with our hypothesis, we found deficits in auditory networks in patients with schizophrenia, similar to those seen by others,9,34
including bilateral temporal lobes, anterior cingulate, and cerebellum. From our results, C3 and C8 characterized a bilateral temporal network containing regions such as the superior temporal and middle temporal gyri while also revealing significant between-group differences between patients and controls. However, we also considered C13 to be a component of interest because it uniquely depicted activation within the transverse temporal gyrus, a region synonymous with the primary auditory cortex, which suggested that this component was possibly engaged in the first-level processing of the auditory task stimulus.35
Although ICA does not allow us to directly address this, a hierarchical relationship could exist where C13 represents a network that modulates the activation of C3 and C8. A recent study using diffusion tensor imaging (DTI) and Granger causality mapping (GCM) found structural and effective connectivity interactions between posterior and anterior STG during a simple sentence comprehension.36
The overlapping regions for C3 and C8 also follow a similar relationship, where C3 shows a stronger posterior STG overlap and C8 exhibiting a more anterior STG localization. These common areas of activation suggest that one of the possible deficits in schizophrenia might occur during the transition from these primary regions to higher level associative regions. Deficits in the maintenance of selective attention during ERP recordings of a similar paradigm support this idea and suggest a possible aberrant connectivity between these frontal-temporal regions.37
A large number of studies have recently focused on the DMN and its relation to other regions within the brain. This network is a hypothesized conglomeration of regions that are recruited during a baseline state of activity when the participant is not engaged in any goal-directed behavior.38
A recent study by Garrity et al,11
using ICA on a similar auditory oddball paradigm, showed that patients with schizophrenia exhibit aberrant connectivity within this particular network, which consists of regions such as the posterior cingulate, precuneus, cingulate gyrus, and cuneus. Using the same analysis methods on a slightly modified oddball paradigm, we found that our results from C22 replicate that previous study. The event-related average for this component shows that patients tend to remain longer than controls in a negatively modulated state followed by a significantly weaker positive modulation. This might suggest that schizophrenia patients have difficulty shifting away from their baseline activity and modulating other networks accordingly to the task at hand. Our overlay of this component with C23 is an attempt at further exploring the interesting similarities and dynamics seen in both networks through ICA and to suggest that the regions normally associated with the DMN could be represented as multiple networks, also stated by Buckner et al.14
In a similar vein to the auditory networks mentioned above, the stronger modulation of C22 could represent a primary baseline network that is further dissociated into other DMN regions as a secondary system. The strong overlap between the posterior cingulate, precuneus/cuneus, and anterior cingulate regions for both components and their negative modulated timecourses suggest a possible functional relationship between them, which our current analysis cannot confirm. However, there are established methods that determine temporal correlations24
and causal relationships39
between these independent components and future studies that probe this relationship could further strengthen this hypothesis.
There were multiple networks involving prefrontal regions such as C6, C17, and C19, including DLPFC, a region known to be involved in working memory.40
This regions is also found to be consistently dysfunctional in schizophrenia41,42
and their unaffected first-degree relatives, suggesting a genetic basis for DLPFC dysfunction.43
The event-related averages for these 3 networks show that patients are exhibiting a level of hypofrontality supported by previous fMRI studies of schizophrenia.44–46
As for C17, there seems to be a large difference not only in the amplitude of the timecourse but also in its shape where patients show an almost absent modulation of the timecourse. A similar component was seen in a previous resting-state analysis of fMRI subjects using ICA and was initially proposed as representative of a dorsal visual stream network.12
If so, this would provide further support to studies that have shown sensory deficits in schizophrenia affecting dorsal visual pathways within the brain.47,48
Involvement of these anatomical areas in target detection tasks, as well as their reductions in schizophrenia, is consistent with the large P300 literature showing P300 amplitude reductions in the illness.49,50
Like the activations associated with target detections, P300 is generated in widespread cortical areas of the lateral prefrontal cortex, temporoparietal junction, and parietal lobes.51
The similarity of these findings across different modalities (fMRI and ERP) suggest that these areas might represent a consistent biological marker in the characterization of schizophrenia.
It is important to stress that the independent cognitive systems found using ICA are not necessarily unique for every analysis. For our purposes, C1, C6, C17, and C25 can be considered components that have been represented in a previous resting-state connectivity study using ICA.12,13,52
C1 exhibited strong robust activation within the cerebellum and has been found in previous ICA analyses of auditory oddball experiments and resting-state analyses.53
Differences within this network also support previous studies that show dysfunction in cerebellar regions for schizophrenia as well as providing some support to the cortico-cerebellar-thalamic-circuitry dysruption theorized by Andreasen et al.54
In C6, the activation of superior and middle frontal gyri along with the anterior cingulate have suggested that this network might represent an executive control system that engages in overriding other regions of the brain to implement cognitive control.55
For C25, activation in the medial visual cortical areas suggests that this network might engage primary visual areas that are further linked to the thalamus for further visual processing.56
A recent study by Calhoun et al53
using ICA showed a similar component in both an auditory oddball task and a resting-state task along with group differences between schizophrenia patients and controls. This highlights one of the advantages of using a method such as ICA, which can extract signals that represent possible functional networks, which could then turn out to be highly task related. Examination of how these systems are modulated might yield the greatest insight into the biologically consistent features of schizophrenia and the cognitive deficits that often accompany them.
Although the availability of a larger subject sample size for fMRI analysis (assuming the task is well controlled and the subjects carefully chosen) leads to a more powerful test of a given hypothesis, the cognitive heterogeneity of schizophrenia suggests that biomarkers relevant to this disorder might be obscured due to group averaging.57
The fact that this analysis was a conglomeration of multiple datasets across different sites adds to the concern of heterogeneity, and we attempted to account for this effect by including site as a covariate in our regression models. There are also certain limitations to ICA that must be considered. Though it gains certain advantages from being a data-driven algorithm, it is limited by the constraint that these functional networks are considered a linear mixture of independent signals. In this regard, nonlinear approaches to ICA might find very different networks that are indicative of meaningful brain activity. Also, functional connectivity differences in a general ICA analyses are limited to the differences found in the modulation of the ICA timecourse, and thus, the exact nature of this difference, (ie, whether a specific region within the network is responsible for these differences) cannot be determined unless higher order analyses such as effective connectivity measures are performed.7,58–60
The medication history of patients with schizophrenia was not accounted for during the fBIRN study. Patients were considered to be evaluated with chronic schizophrenia and stabilized with medication by a medically licensed physician. However, a detailed history of medications would provide the possibility for interesting correlational analyses with the results from ICA as well as accounting for possible confounds caused by these medications. It would also allow us to determine if particular psychotropics used by certain patients had a significant affect on the results of our ICA analysis. The effects of psychotropics on cognitive activity are often hard to assess, but they all have in common the ability to block D2 receptors. However, these medications differ in the potency of this blockade and vary in the types of receptors that are affected. Thus, a medication that is highly muscarinic may have some cognitive slowing and could present a potential confound in our analysis. Another possible limitation of our study can be in our decision to group schizoaffective patients with schizophrenia patients. If there are strong neurobiological differences in the pathology of one versus the other, it could then represent a potential confound in our analysis.
The elucidation of cognitive deficits in schizophrenia has been an important step toward parsing the neurocognitive pathology of this highly complex disorder. The analysis of these deficits in neuroimaging studies can benefit from the application of various signal-processing techniques. Using ICA, we were able to identify functionally independent networks that coincide with regions previously associated with the auditory oddball paradigm. We identified multiple networks that are implicated in schizophrenia that might play a significant role in the characterization of this disorder. We found a strong concentration of networks that lie within the DLPFC regions along with bilateral temporal lobes. Our results also showed that the DMN was implicated in schizophrenia, confirming previous studies that have found similar results using ICA. Future studies could benefit from a focus on the interaction of these networks and their distinction from one another, which could further elucidate important cognitive characteristics associated with schizophrenia. The benefits of our approach are in the large-scale analysis of multiple subjects using an approach that is able to identify deficits in functionally connected networks that are not contingent on a predefined hemodynamic response. Our findings support and extend the numerous studies that have identified similar regions associated with cognitive deficits in patients with schizophrenia.